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+ + +
+ +
+
+
+
    +
    • +
  • Overview: module code
    • +
  • +
    • +
+
+
+
+
+ +

All modules for which code is available

+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/_modules/libtsm/pitchshift.html b/docs/build/html/_modules/libtsm/pitchshift.html new file mode 100644 index 0000000..4197da0 --- /dev/null +++ b/docs/build/html/_modules/libtsm/pitchshift.html @@ -0,0 +1,272 @@ + + + + + + libtsm.pitchshift — libtsm 1.1.1 documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for libtsm.pitchshift

+"""
+Description: libtsm pitch-shifting functions
+Contributors: Sebastian Rosenzweig, Simon Schwär, Jonathan Driedger, Meinard Müller
+License: The MIT license, https://opensource.org/licenses/MIT
+This file is part of libtsm (https://www.audiolabs-erlangen.de/resources/MIR/2021-DAFX-AdaptivePitchShifting)
+"""
+
+import numpy as np
+import scipy as sc
+import scipy.signal
+import scipy.interpolate
+from fractions import Fraction as frac
+from .tsm import hps_tsm
+from .utils import normalize_length
+
+
+
[docs]def pitch_shift_original(x, n, Fs=22050) -> np.ndarray:
+    """
+    Pitch modification algorithm via time-scale modification. The input signal is stretched via TSM and then resampled.
+    The code closely follows the Matlab implementation.
+
+    Parameters
+    ----------
+    x : np.ndarray [shape=(N, )], real - valued
+        Signal to be transformed
+
+    n : int
+        Amount of pitch shifting to be applied, given in cents. Positive n indicates pitch rising, negative n a pitch
+        lowering
+
+    Fs : int
+        Sampling rate of the input audio signal x
+
+    Returns
+    -------
+    y : np.ndarray [shape=(L,1)], real - valued
+        The time-scale modified output signal
+    """
+
+    if len(x.shape) == 1:
+        x = x.reshape(-1, 1)
+
+    alpha = np.power(np.power(2, 1 / 12), (n / 100))
+    y_tsm = hps_tsm(x, alpha, Fs=Fs)
+    const = Fs / np.around(alpha * Fs)
+    f = frac(str(const)).limit_denominator(100)
+    p = f.numerator
+    q = f.denominator
+    x_f = sc.signal.resample_poly(y_tsm, int(p), int(q), axis=0)  # deviations from Matlab's resample()
+    y = normalize_length(x_f, x.shape[0])
+
+    return y

+
+
+
[docs]def pitch_shift(x, p, t_p=None, Fs=22050, order="res-tsm", **kwargs) -> np.ndarray:
+    """
+    (Non-linear) pitch-shifting via time-scale modification and resampling.
+
+    Parameters
+    ----------
+    x : np.ndarray [shape=(N, )], real - valued
+        Signal to be transformed
+
+    p : float or np.ndarray [shape=(M,)], real - valued
+        Amount of pitch shifting to be applied, given in cents. Positive p indicates pitch rising, negative p a pitch
+        lowering.
+
+    t_p : np.ndarray [shape=(M,)], real - valued
+        Array of time instances in seconds for adaptive pitch shifting, same length as p. If t==None, a fixed
+        pitch-shift is assumed.
+
+    Fs : int
+        Sampling rate of the input audio signal x
+
+    order : Order of TSM and resampling, either "res-tsm" or "tsm-res".
+
+    **kwargs : Parameters for hps_tsm
+
+    Returns
+    -------
+    y : np.ndarray [shape=(L,1)], real - valued
+        The time-scale modified output signal
+    """
+
+    if len(x.shape) == 1:
+        x = x.reshape(-1, 1)
+
+    t_x = np.linspace(0, (len(x) - 1) / Fs, len(x))
+
+    if not np.isscalar(p):
+        if t_p is None:
+            raise Exception("t must be specified if p is an array!")
+        if len(p) != len(t_p):
+            raise Exception("t must have the same length as p!")
+        if t_p[0] != 0:  # time axis should start with 0
+            t_p = t_p.astype(float)
+            t_p = np.insert(t_p, 0, 0)
+            p = np.insert(p, 0, 0)
+        if t_p[-1] != t_x[-1]:  # time axis should end with the last time instance
+            t_p = t_p.astype(float)
+            t_p = np.insert(t_p, len(t_p), t_x[-1])
+            p = np.insert(p, len(p), 0)
+
+    # account for sign change when order of resampling and TSM is exchanged
+    if order == "res-tsm":
+        alpha = 2 ** (-p / 1200)
+    elif order == "tsm-res":
+        alpha = 2 ** (p / 1200)
+    else:
+        raise Exception("Order must be either res-tsm or tsm-res!")
+
+    # convert pitch shift in cents to (non-linear) time-stretch function tau
+    if np.isscalar(p):
+        tau = np.array([[0, 0], [x.shape[0] - 1, x.shape[0] * alpha - 1]]) / Fs  # given in seconds
+    else:
+        # compute tau
+        tau = np.zeros((len(alpha), 2))
+        tau[:, 0] = t_p
+
+        for i in range(1, len(alpha)):
+            dt = tau[i, 0] - tau[i - 1, 0]
+            tau[i, 1] = dt * alpha[i-1] + tau[i - 1, 1]
+
+    # Pitch-shifting
+    if order == "res-tsm":
+        # (Non-linear) Resampling
+        fi = sc.interpolate.interp1d(tau[:, 0], tau[:, 1], kind='linear', fill_value="extrapolate")
+        time_input = fi(t_x)
+        fi = sc.interpolate.interp1d(time_input, x[:, 0], kind='cubic', fill_value="extrapolate")
+        t_res = np.arange(0, tau[-1, 1] + 1 / Fs, 1 / Fs)
+        y_ps = fi(t_res)
+
+        # use inverse tau points as anchor points for TSM
+        anchor_points = np.ceil(tau * Fs).astype(int)
+        anchor_points = np.flip(anchor_points, axis=1)
+        # only keep unique indices
+        anchor_points = anchor_points[np.unique(anchor_points[:, 1], return_index=True)[1], :]
+
+        # Time-Scale Modification
+        y_ps = hps_tsm(y_ps, anchor_points, Fs=Fs, **kwargs)
+
+    elif order == "tsm-res":
+        # compute anchor points
+        anchor_points = np.ceil(tau * Fs).astype(int)
+        # only keep unique indices
+        anchor_points = anchor_points[np.unique(anchor_points[:, 1], return_index=True)[1], :]
+
+        # Time-Scale Modification
+        y_tsm = hps_tsm(x, anchor_points, Fs=Fs, **kwargs)
+
+        # (Non-linear) resampling
+        time_output = np.linspace(0, (y_tsm.shape[0] - 1) / Fs, y_tsm.shape[0])
+        fi = sc.interpolate.interp1d(tau[:, 1], tau[:, 0], kind='linear', fill_value="extrapolate")
+        time_input = fi(time_output)
+        fi = sc.interpolate.interp1d(time_input, y_tsm[:, 0], kind='cubic', fill_value="extrapolate")
+        y_ps = fi(t_x)
+
+    # crop if pitch-shifted signal is longer than x
+    y_ps = y_ps.reshape(-1, 1)[:len(x), :]
+
+    return y_ps

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/_modules/libtsm/tsm.html b/docs/build/html/_modules/libtsm/tsm.html new file mode 100644 index 0000000..11c4088 --- /dev/null +++ b/docs/build/html/_modules/libtsm/tsm.html @@ -0,0 +1,617 @@ + + + + + + libtsm.tsm — libtsm 1.1.1 documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for libtsm.tsm

+"""
+Description: libtsm time-scale modification functions
+Contributors: Sebastian Rosenzweig, Simon Schwär, Jonathan Driedger, Meinard Müller
+License: The MIT license, https://opensource.org/licenses/MIT
+This file is part of libtsm (https://www.audiolabs-erlangen.de/resources/MIR/2021-DAFX-AdaptivePitchShifting)
+"""
+
+import numpy as np
+import scipy.interpolate
+from .utils import win, stft, istft, cross_corr, hps, find_peaks
+
+
+
[docs]def pv_tsm(x, alpha, syn_hop=512, win_length=2048, win_beta=1, Fs=22050, zero_pad=0, restore_energy=False,
+           fft_shift=False, phase_locking=False) -> np.ndarray:
+    """Time scale modification based on a phase vocoder
+
+    Rescales the time-axis of the input signal x according to the time-stretch function alpha
+    without altering the pitch of x.
+
+    Parameters
+    ----------
+    x : np.ndarray [shape=(N, C)], real-valued
+        Signal to be transformed, second dimension is an optional channel dimension
+
+    alpha : float or np.ndarray [shape=(S, 2)]
+        Time stretch function, given by a constant (float) or a set of S anchor points (int).
+        A valid anchor point sequence
+            (1) contains only non-negative values,
+            (2) both sequences along the first axis are strictly increasing,
+            (3) starts with position (m, 0), where m is an intereger >= 0.
+        These conditions will be checked and an error is thrown if they are not met.
+        See `libtsm.ensure_validity` for one way to ensure that condition (2) is fulfilled.
+
+    syn_hop : int
+        hop size of the synthesis window
+
+    win_length : int
+        length of analysis and synthesis window for STFT
+
+    win_beta : int
+        exponent of sin^beta window
+
+    Fs : int
+        Sampling rate of the input audio signal x
+
+    zero_pad : int
+        For FFT. Number of zeros padded to the window to increase the fft size
+
+    restore_energy : bool
+        For FFT. When True, rescales every windowed synthesis frame to compensate for synthesis energy leakage
+
+    fft_shift : bool
+        For FFT. When True, applies a circular shift to each frame of half its length, prior to computing the FFT
+
+    phase_locking : bool
+        when True, Applies identity phase locking
+
+    Returns
+    -------
+    y : np.ndarray [shape=(L,1)], real - valued
+    """
+
+    # Pre-calculations
+    window = win(win_length, win_beta)
+
+    w = np.concatenate((np.zeros(int(np.floor(zero_pad / 2))), window, np.zeros(int(np.floor(zero_pad / 2)))))
+    win_len = len(w)
+    win_len_half = win_len // 2
+
+    if len(x.shape) == 1:
+        x = x.reshape(-1, 1)
+
+    num_of_chan = x.shape[1]
+
+    # Time-stretch function
+    if np.isscalar(alpha):
+        anchor_points = np.array([[0, 0], [int(x.shape[0]) - 1, int(np.ceil(alpha * x.shape[0])) - 1]])
+    else:
+        anchor_points = alpha.astype(int)
+
+    output_length = anchor_points[-1, 1] + 1
+    syn_win_pos = np.arange(0, output_length + win_len_half, syn_hop)  # positions of the synthesis winLenHalf windows
+    # in the output
+
+    fi = scipy.interpolate.interp1d(anchor_points[:, 1], anchor_points[:, 0], kind='linear', fill_value='extrapolate')
+    ana_win_pos = fi(syn_win_pos)
+    ana_win_pos = np.round(ana_win_pos).astype(int)  # positions of the analysis windows in the input
+    ana_hop = np.append([0], ana_win_pos[1:] - ana_win_pos[:-1])  # analysis hop sizes
+
+    # check conditions
+    assert anchor_points[0,1] == 0, "First sample for target sequence must be zero."
+    assert anchor_points[0,0] >= 0, "All anchor points must be non-negative."
+    assert np.all(ana_hop[1:] > 0), "The anchor point sequences must be stricly increasing for both source and target."
+
+    # Phase Vocoder
+    y = np.zeros((output_length, num_of_chan))  # initialize output
+
+    for c in range(num_of_chan):  # loop over channels
+
+        x_c = x[:, c]
+
+        # STFT
+        X, f, t = stft(x_c, ana_hop=ana_win_pos, win_length=win_length, win_beta=win_beta, Fs=Fs, num_of_frames=-1,
+                       fft_shift=fft_shift, zero_pad=zero_pad)
+
+        # Phase adaptation
+        Y = np.zeros(X.shape, dtype=complex)  # the spectrogram of the output
+        Y[:, 0] = X[:, 0]  # phase initialization
+        N = len(w)
+        k = np.arange(0, N // 2 + 1)  # center frequencies of the N/2+1 first bins of the spectrum in
+        # 'oscillations per frame'
+        omega = 2 * np.pi * k / N  # phase advances per sample for the frequencies k
+
+        for i in range(1, X.shape[1]):
+            dphi = omega * ana_hop[i]  # expected phase advances from the last to the current input frame
+            ph_curr = np.angle(X[:, i])  # phases of the current input frame
+            ph_last = np.angle(X[:, i - 1])  # phases of the last input frame
+            hpi = (ph_curr - ph_last) - dphi  # heterodyned phase increments
+            # hpi = np.array([num - 2 * np.pi * round(num/(2*np.pi)) for num in hpi])  # reduce to the range -pi:pi,
+            # np.round() deviates from Matlab's round()
+            # matlab_round = np.vectorize(lambda v: round(v))
+            hpi = hpi - 2 * np.pi * np.round(hpi / (2 * np.pi))  # reduce to the range -pi:pi
+            ipa_sample = omega + hpi / ana_hop[i]  # instantaneous phase advances per sample
+            ipa_hop = ipa_sample * syn_hop  # instantaneous phase advances per synthesis hopsize
+            ph_syn = np.angle(Y[:, i - 1])  # phases of the last synthesized frame
+
+            # We now compute a phasor that rotates the phase angles of the current input frame by angles theta such
+            # that no phase discontinuities occur when re-synthesizing the resulting spectrogram with the synthesis
+            # hopsize
+            if not phase_locking:  # standard phase vocoder: the phase continuity of every bin is preserved separately
+                theta = ph_syn + ipa_hop - ph_curr  # phases of the last output frame Instantaneous phase advance
+                # Phases of the current input frame
+
+            else:  # Phase vocoder with identity phase locking: the phase relationships from the input frame are
+                # partially preserved by 'locking' the phases of bins in the region of influence of a peak in the
+                # sprectrum to the phase of the peak's bin
+                p, irs, ire = find_peaks(X[:, i])  # Get the peaks in the spectrum together with their regions of
+                # influence
+                theta = np.zeros(Y[:, i].shape)
+
+                for n in range(0, len(p)):
+                    theta[irs[n]:ire[n]] = ph_syn[p[n]] + ipa_hop[p[n]] - ph_curr[p[n]]  # Phases of the last
+                    # output frame, Instantaneous phase advance, Phases of the current input frame
+
+            Y[:, i] = np.exp(1j * theta) * X[:, i]
+
+        # ISTFT
+        y_c = istft(Y, syn_hop=syn_hop, win_length=win_length, win_beta=win_beta, zero_pad=zero_pad, num_of_iter=1,
+                    orig_sig_len=output_length, restore_energy=restore_energy, fft_shift=fft_shift)
+
+        y[:, c] = y_c[:, 0]
+
+    return y

+
+
+
[docs]def wsola_tsm(x, alpha, syn_hop=512, win_length=1024, win_beta=2, tol=512) -> np.ndarray:
+    """
+    Waveform Similarity Overlap and Add (WSOLA) algorithm that rescales the time-axis of the input signal x
+    according to the time-stretch function s without altering the pitch of x.
+
+    Parameters
+    ----------
+    x : np.ndarray [shape=(N,num_of_chan)], real - valued
+        Signal to be transformed
+
+    alpha : float or np.ndarray [shape=(S, 2)]
+        Time stretch function, given by a constant (float) or a set of S anchor points (int).
+        A valid anchor point sequence
+            (1) contains only non-negative values,
+            (2) both sequences along the first axis are strictly increasing,
+            (3) starts with position (m, 0), where m is an intereger >= 0.
+        These conditions will be checked and an error is thrown if they are not met.
+        See `libtsm.ensure_validity` for one way to ensure that condition (2) is fulfilled.
+
+    syn_hop : int
+        hop size of the synthesis window
+
+    win_length : int
+        length of the analysis and synthesis window
+
+    win_beta : int
+        exponent of sin^beta window
+
+    tol : int
+        Amount of samples the window will be shifted to avoid phase discontinuities when overlap-adding to form the
+        output signal
+
+    Returns
+    -------
+    y : np.ndarray [shape=(L,num_of_chan)], real - valued
+        The time-scale modified output signal
+    """
+
+    # Pre-calculations
+    window = win(win_length, win_beta)
+
+    w = window
+    win_len = len(w)
+    win_len_half = np.around(win_len / 2).astype(int)
+
+    if len(x.shape) == 1:
+        x = x.reshape(-1, 1)
+
+    num_of_chan = x.shape[1]
+
+    # Time-stretch function
+    if np.isscalar(alpha):
+        anchor_points = np.array([[0, 0], [int(x.shape[0]) - 1, int(np.ceil(alpha * x.shape[0])) - 1]])
+    else:
+        anchor_points = alpha.astype(int)
+
+    output_length = anchor_points[-1, 1] + 1
+    syn_win_pos = np.arange(0, output_length + win_len_half, syn_hop)  # positions of the synthesis winLenHalf
+    # windows in the output
+
+    fi = scipy.interpolate.interp1d(anchor_points[:, 1], anchor_points[:, 0], kind='linear', fill_value='extrapolate')
+    ana_win_pos = fi(syn_win_pos)
+    ana_win_pos = np.round(ana_win_pos).astype(int)  # positions of the analysis windows in the input
+    ana_hop = np.append([0], ana_win_pos[1:] - ana_win_pos[:-1])  # analysis hop sizes
+
+    # check conditions
+    assert anchor_points[0,1] == 0, "First sample for target sequence must be zero."
+    assert anchor_points[0,0] >= 0, "All anchor points must be non-negative."
+    assert np.all(ana_hop[1:] > 0), "The anchor point sequences must be stricly increasing for both source and target."
+
+    # WSOLA
+    y = np.zeros((output_length, num_of_chan))  # initialize output
+    min_fac = np.min(syn_hop / ana_hop[1:])  # the minimal local stretching factor
+    # to avoid that we access x outside its range, we need to zero pad it appropriately
+    x = np.pad(x, [(win_len_half + tol, int(np.ceil(1 / min_fac)) * win_len + tol), (0, 0)])
+    ana_win_pos += tol  # compensate for the extra 'tol' padded zeros at the beginning of x
+
+    for c in range(num_of_chan):  # loop over channels
+        x_c = x[:, c]
+        y_c = np.zeros((output_length + 2 * win_len, 1))  # initialize the output signal
+        ow = np.zeros((output_length + 2 * win_len, 1))  # keep track of overlapping windows
+        delay = 0  # shift of the current analysis window position
+
+        for i in range(len(ana_win_pos) - 1):
+            # OLA
+            curr_syn_win_ran = np.arange(syn_win_pos[i], syn_win_pos[i] + win_len, dtype=int)  # range of current
+            # synthesis window
+            curr_ana_win_ran = np.arange(ana_win_pos[i] + delay, ana_win_pos[i] + win_len + delay, dtype=int)  # range
+            # of the current analysis window, shift by 'del' offset
+            y_c[curr_syn_win_ran, 0] += x_c[curr_ana_win_ran] * w  # overlap and add
+            ow[curr_syn_win_ran, 0] += w  # update the sum of overlapping windows
+            nat_prog = x_c[curr_ana_win_ran + syn_hop]  # 'natural progression' of the last copied audio segment
+            next_ana_win_ran = np.arange(ana_win_pos[i + 1] - tol, ana_win_pos[i + 1] + win_len + tol, dtype=int)  #
+            # range where the next analysis window could be located (including the tolerance region)
+            x_next_ana_win_ran = x_c[next_ana_win_ran]  # corresponding segment in x
+
+            # Cross Correlation
+            cc = cross_corr(x_next_ana_win_ran, nat_prog, win_len)  # compute the cross correlation
+            max_index = np.argmax(cc)  # pick the optimizing index in the cross correlation
+            delay = tol - max_index  # infer the new 'delay'
+
+        # process last frame
+        y_c[syn_win_pos[-1]:syn_win_pos[-1] + win_len, 0] += x_c[ana_win_pos[i] + delay:ana_win_pos[
+                                                                                            i] + win_len + delay] * w
+        ow[syn_win_pos[-1]:syn_win_pos[-1] + win_len, 0] += w
+
+        # re-normalize the signal by dividing by the added windows
+        ow[ow < 10 ** (-3)] = 1  # avoid potential division by zero
+        y_c /= ow
+
+        # remove zero-padding at the beginning
+        y_c = y_c[win_len_half:]
+
+        # remove zero-padding at the end
+        y_c = y_c[0:output_length]
+
+        y[:, c] = y_c[:, 0]
+
+    return y

+
+
+
[docs]def hps_tsm(x, alpha, Fs=22050, hps_ana_hop=256, hps_win_length=1024, hps_win_beta=2, hps_zero_pad=0,
+            hps_fil_len_harm=10, hps_fil_len_perc=10, pv_syn_hop=512, pv_win_length=2048, pv_win_beta=2, pv_zero_pad=0,
+            pv_restore_energy=False, pv_fft_shift=False, ola_syn_hop=128, ola_win_length=256, ola_win_beta=2) \
+        -> np.ndarray:
+    """
+    Time Scale Modification algorithm based on Harmonic - Percussive separation. After separation is
+    performed, the algorithm uses two phase vocoder TSM and WSOLA TSM algorithms for the Harmonic and percussive part
+    separately.
+
+    Parameters
+    ----------
+    x : np.ndarray [shape=(N, )], real - valued
+        Signal to be transformed
+
+    alpha : float or np.ndarray [shape=(S, 2)]
+        Time stretch function, given by a constant (float) or a set of S anchor points (int).
+        A valid anchor point sequence
+            (1) contains only non-negative values,
+            (2) both sequences along the first axis are strictly increasing,
+            (3) starts with position (m, 0), where m is an intereger >= 0.
+        These conditions will be checked and an error is thrown if they are not met.
+        See `libtsm.ensure_validity` for one way to ensure that condition (2) is fulfilled.
+
+    Fs : int
+        Sampling rate
+
+    hps_ana_hop : int
+        hop size for HPS
+
+    hps_win_length : int
+        window length for HPS
+
+    hps_win_beta : int
+        exponent of sin^beta window
+
+    hps_zero_pad : int
+        For FFT. Number of zeros padded to the window to increase the fft size
+
+    hps_fil_len_harm: int
+        Length of the median filter in time direction.
+
+    hps_fil_len_perc: int
+        Length of the median filter in frequency direction.
+
+    pv_syn_hop : int
+        hop size for synthesize windows of phase vocoder
+
+    pv_win_length : int
+        window length for phase vocoder
+
+    pv_win_beta : int
+        exponent of sin^beta window
+
+    pv_zero_pad : int
+        phase vocoder zero padding
+
+    pv_restore_energy : bool
+        restore energy of signal in phase vocoder
+
+    pv_fft_shift : bool
+        fft shift in phase vocoder
+
+    ola_syn_hop : int
+        synthesis hop size of OLA
+
+    ola_win_length : int
+        window length for OLA
+
+    ola_win_beta : int
+        exponent of sin^beta window
+
+    Returns
+    -------
+    y : np.ndarray [shape=(L,1)], real - valued
+        The time-scale modified output signal
+    """
+
+    # Harmonic-Percussive Separation
+    x_harm, x_perc = hps(x, ana_hop=hps_ana_hop, win_length=hps_win_length, win_beta=hps_win_beta, Fs=Fs,
+                         zero_pad=hps_zero_pad, fil_len_harm=hps_fil_len_harm, fil_len_perc=hps_fil_len_perc,
+                         masking_mode='binary')
+
+    # Phase Vocoder for harmonic part
+    y_harm = pv_tsm(x_harm, alpha=alpha, syn_hop=pv_syn_hop, win_length=pv_win_length, win_beta=pv_win_beta, Fs=Fs,
+                    zero_pad=pv_zero_pad, restore_energy=pv_restore_energy, fft_shift=pv_fft_shift, phase_locking=True)
+
+    # OLA for percussive part
+    y_perc = wsola_tsm(x_perc, alpha=alpha, syn_hop=ola_syn_hop, win_length=ola_win_length, win_beta=ola_win_beta,
+                       tol=0)
+
+    # Synthesis
+    y = y_harm + y_perc
+
+    return y

+
+
+
[docs]def pv_int_tsm(x, alpha, syn_hop=512, win_length=2048, win_beta=2, Fs=22050, zero_pad=-1, restore_energy=False,
+               fft_shift=True) -> np.ndarray:
+    """
+    Phase Vocoder Time scale modification algorithm, that rescales the time-axis of the input signal x
+    according to the time-stretch function s without altering the pitch of x. This algorithm is optimized for integer
+    values of the time stretching function.
+
+    Parameters
+    ----------
+    x : np.ndarray [shape=(N,)], real - valued
+        Signal to be transformed
+
+    alpha : int or np.ndarray
+        Time stretch factor
+
+    syn_hop : int
+        hop size of the synthesis window
+
+    win_length : int
+        length of analysis and synthesis window for STFT
+
+    win_beta : int
+        exponent of sin^beta window
+
+    Fs : int
+        Sampling rate of the input audio signal x
+
+    zero_pad : int
+        For FFT. Number of zeros padded to the window to increase the fft size
+
+    restore_energy : bool
+        For FFT. When True, rescales every windowed synthesis frame to compensate for synthesis energy leakage
+
+    fft_shift: bool
+        For FFT. When True, applies a circular shift to each frame of half its length, prior to computing the FFT
+
+
+    Returns
+    -------
+    y : np.ndarray [shape=(L,1)], real - valued
+        The time-scale modified output signal
+    """
+
+    # Pre-Calculations
+    window = win(win_length, win_beta)
+
+    if len(x.shape) == 1:
+        x = x.reshape(-1, 1)
+
+    num_of_chan = x.shape[1]
+
+    if zero_pad == -1:
+        zero_pad = alpha * window.shape[0] // 2
+
+    wn = np.hstack((np.zeros((int(np.floor(zero_pad / 2)))),
+                    window,
+                    np.zeros((int(np.floor(zero_pad / 2))))))
+    win_len = wn.shape[0]
+    win_len_half = int(np.round(win_len / 2))
+
+    if (np.isscalar(alpha)) and (np.mod(alpha, 1) == 0) and (alpha >= 1):
+        anchor_points = np.array([[0, 0], [int(x.shape[0]), int(np.ceil(alpha * x.shape[0]))]])
+    else:
+        raise Exception("alpha needs to be an integer >= 1 !")
+
+    while np.mod(syn_hop, alpha) != 0:
+        syn_hop = syn_hop + 1
+
+    output_length = int(anchor_points[-1, 1])
+    output_window_pos = np.arange(0, output_length + win_len_half, syn_hop)  # positions of the synthesis winLenHalf
+    # windows in the output
+    input_window_pos = output_window_pos // alpha
+
+    y = np.zeros((output_length, num_of_chan))
+
+    for c in range(num_of_chan):
+        # stft
+        X, f, t = stft(x[:, c], ana_hop=input_window_pos, win_length=win_length, win_beta=win_beta, Fs=Fs,
+                       num_of_frames=-1, fft_shift=fft_shift, zero_pad=zero_pad)
+
+        # Phase Adaption
+        Y = np.abs(X) * np.exp(1j * alpha * np.angle(X))
+
+        # istft
+        y_c = istft(Y, syn_hop=syn_hop, win_length=win_length, win_beta=win_beta, Fs=Fs, zero_pad=zero_pad,
+                    num_of_iter=1, orig_sig_len=output_length, restore_energy=restore_energy, fft_shift=fft_shift)
+
+        y[:, c] = y_c[:, 0]
+
+    return y

+
+
+
[docs]def two_step_tsm(x, alpha, Fs=22050, order='exact-coarse') -> np.ndarray:
+    """
+    Time Scale Modification algorithm, where the signal is stretched by the integer and decimal part
+    of the time stretch function using two different algorithms.
+
+    Parameters
+    ----------
+    x : np.ndarray [shape=(N, )], real - valued
+        Signal to be transformed
+
+    alpha : float
+        Scalar time stretch factor
+
+    Fs : int
+        Sampling rate of the input audio signal x
+
+    order : 'exact-coarse' or 'coarse-exact'
+        Decides which of the two time stretching functions will be computed first, coarse corresponding to the integer
+        part
+
+
+    Returns
+    -------
+    y : np.ndarray [shape=(L,num_of_chan)], real - valued
+        The time-scale modified output signal
+    """
+    if len(x.shape) == 1:
+        x = x.reshape(-1, 1)
+
+    alpha_rough = np.max([1, np.round(alpha)]).astype(int)
+    alpha_exact = alpha / alpha_rough
+
+    if order == 'exact-coarse':
+        y_exact = hps_tsm(x, alpha_exact, Fs=Fs)
+        y = pv_int_tsm(y_exact[:, 0], alpha_rough, Fs=Fs)
+
+    elif order == 'coarse-exact':
+        y_coarse = pv_int_tsm(x, alpha_rough, Fs=Fs)
+        y = hps_tsm(y_coarse[:, 0], alpha_exact, Fs=Fs)
+    else:
+        raise Exception("Invalid order!")
+
+    return y

+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/_modules/libtsm/utils.html b/docs/build/html/_modules/libtsm/utils.html new file mode 100644 index 0000000..5372fc2 --- /dev/null +++ b/docs/build/html/_modules/libtsm/utils.html @@ -0,0 +1,728 @@ + + + + + + libtsm.utils — libtsm 1.1.1 documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for libtsm.utils

+"""
+Description: libtsm utility functions
+Contributors: Sebastian Rosenzweig, Simon Schwär, Jonathan Driedger, Meinard Müller
+License: The MIT license, https://opensource.org/licenses/MIT
+This file is part of libtsm (https://www.audiolabs-erlangen.de/resources/MIR/2021-DAFX-AdaptivePitchShifting)
+"""
+
+import numpy as np
+import scipy as sc
+import scipy.signal
+from typing import Tuple
+
+
+
[docs]def ensure_validity(alpha, syn_hop=128):
+    """Remove points from a sequence of anchor points to ensure a valid TSM input
+
+    This function removes any anchor points that lead to a non-monotonous increase of the analysis window and
+    returns a new sequence of anchor points that is a valid TSM input.
+
+    Note that, depending on the data, this is not a sensible way to ensure a valid TSM input. Often, it may be
+    advisable to use coarser and semantically meaningful positions for the anchor points.
+
+    Parameters
+    ----------
+    alpha : float or np.ndarray [shape=(S, 2)]
+        Time stretch function, given by a set of S anchor points (int).
+
+    syn_hop : int
+        (smallest) hop size of the synthesis window (default: 128)
+    """
+    d_ana = np.diff(alpha[:,0])
+    d_syn = np.diff(alpha[:,1])
+    not_too_steep = np.pad(np.round(d_syn / d_ana) <= syn_hop, (1,0), constant_values=True)
+
+    # call recursively to remove consecutive points that are too steep
+    if not np.all(not_too_steep):
+        return ensure_validity(alpha[not_too_steep,:], syn_hop)
+    else:
+        return alpha

+
+
+
[docs]def win(win_len, beta) -> np.ndarray:
+    """
+    Generates a sin^beta window.
+
+    Parameters
+    ----------
+    win_len : int
+             length of the window
+
+    beta :   int
+             Exponent of the window
+    Returns
+    -------
+    w : np.ndarray [shape=(win_len, )]
+        The desired window
+    """
+    w = np.sin((np.pi * np.arange(0, win_len)) / win_len) ** beta
+
+    return w

+
+
+
[docs]def hps(x, ana_hop=256, win_length=1024, win_beta=2, Fs=22050, zero_pad=0, fil_len_harm=10, fil_len_perc=10,
+        masking_mode='binary') -> Tuple[np.ndarray, np.ndarray]:
+    """
+    Harmonic - Percussive separation usign median filters.
+
+    Parameters
+    ----------
+    x : np.ndarray [shape=(N, )], real - valued
+        Signal to be transformed
+
+    ana_hop : int
+        hop size of the synthesis window
+
+    win_length : int
+        length of analysis and synthesis window for STFT
+
+    win_beta : int
+        exponent of sin^beta window
+
+    Fs : int
+        Sampling rate of the input audio signal x
+
+    zero_pad : int
+        For FFT. Number of zeros padded to the window to increase the fft size
+
+    fil_len_harm: int
+        Length of the median filter in time direction. A shorter filter makes it more likely
+        that the signal is interpreted as harmonic.
+
+    fil_len_perc: int
+        Length of the median filter in frequency direction. A shorter filter makes it more likely
+        that the signal is interpreted as percussive.
+
+    masking_mode : either "binary" or "relative"
+        Selects Harmonic Percussive separation masking mode (soft or binary masking)
+
+    Returns
+    -------
+    x_harm : np.ndarray
+        Harmonic Component of input signal x
+
+    x_perc : np.ndarray
+        Percussive Component of input signal x
+    """
+    # Pre calculations
+    window = win(win_length, win_beta)
+
+    if len(x.shape) == 1:
+        x = x.reshape(-1, 1)
+
+    num_of_chan = x.shape[1]
+
+    #  harmonic-percussive separation
+    x_harm = np.zeros(x.shape)  # Initialize output
+    x_perc = np.zeros(x.shape)  # Initialize output
+
+    for c in range(num_of_chan):  # loop over channels
+        x_c = x[:, c]
+
+        # stft
+        spec, f, t = stft(x_c, ana_hop=ana_hop, win_length=win_length, win_beta=win_beta, Fs=Fs, num_of_frames=-1,
+                          fft_shift=False, zero_pad=zero_pad)
+        mag_spec = np.abs(spec)
+
+        # harmonic-percussive separation
+        mag_spec_perc = median_filter(mag_spec, fil_len_perc, 0)
+        mag_spec_harm = median_filter(mag_spec, fil_len_harm, 1)
+
+        if masking_mode == 'binary':
+            mask_harm = mag_spec_harm > mag_spec_perc
+            mask_perc = mag_spec_harm <= mag_spec_perc
+
+        elif masking_mode == 'relative':
+            mask_harm = mag_spec_harm / (mag_spec_harm + mag_spec_perc + np.finfo(float).eps)
+            mask_perc = mag_spec_perc / (mag_spec_harm + mag_spec_perc + np.finfo(float).eps)
+
+        else:
+            raise Exception('masking mode must either be "binary" or "relative"!')
+
+        spec_harm = mask_harm * spec
+        spec_perc = mask_perc * spec
+
+        # istft
+        x_harm_c = istft(spec_harm, syn_hop=ana_hop, win_length=win_length, win_beta=win_beta, Fs=Fs, zero_pad=zero_pad,
+                         num_of_iter=1, orig_sig_len=x.shape[0], restore_energy=False, fft_shift=False)
+        x_perc_c = istft(spec_perc, syn_hop=ana_hop, win_length=win_length, win_beta=win_beta, Fs=Fs, zero_pad=zero_pad,
+                         num_of_iter=1, orig_sig_len=x.shape[0], restore_energy=False, fft_shift=False)
+
+        x_harm[:, c] = x_harm_c[:, 0]
+        x_perc[:, c] = x_perc_c[:, 0]
+
+    return x_harm, x_perc

+
+
+
[docs]def median_filter(X, filt_len, dim) -> np.ndarray:
+    """
+    Median filter implementation.
+
+    Parameters
+    ----------
+    X : np.ndarray
+        Spectrogram
+    filt_len : int
+        Median filter length
+    dim : int
+        Dimension in which median filter should be applied
+
+    Returns
+    -------
+    Y : np.ndarray
+        Median-filtered spectrogram
+
+    """
+    s = X.shape
+    Y = np.zeros(s)
+
+    if dim == 0:
+        X_padded = np.vstack((np.zeros((int(np.floor(filt_len / 2)), s[1])),
+                              X,
+                              np.zeros((int(np.ceil(filt_len / 2)), s[1]))))
+        for i in range(s[0]):
+            Y[i, :] = np.median(X_padded[i:i + filt_len, :], axis=0)
+
+    elif dim == 1:
+        X_padded = np.hstack((np.zeros((s[0], int(np.floor(filt_len / 2)))),
+                              X,
+                              np.zeros((s[0], int(np.ceil(filt_len / 2))))))
+        for i in range(s[1]):
+            Y[:, i] = np.median(X_padded[:, i:i + filt_len], axis=1)
+
+    else:
+        raise Exception("Invalid div!")
+
+    return Y

+
+
+
[docs]def stft(x, ana_hop=2048, win_length=4096, win_beta=2, Fs=22050, num_of_frames=-1, fft_shift=False, zero_pad=0) -> \
+        Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """
+    Computes the Short-Time Fourier Transform (STFT) of the input audio signal.
+
+    Parameters
+    ----------
+    x : np.ndarray, real-valued
+        Signal to be transformed
+
+    ana_hop : int or np.ndarray
+        hop size of the analysis window
+
+    win_length : int
+        length of analysis window for STFT
+
+    win_beta : int
+        exponent of sin^beta window
+
+    Fs : int
+        Sampling rate of the input audio signal x
+
+    num_of_frames : int
+        Fixes the number of FFT frames to be computed
+
+    fft_shift: bool
+        For FFT. When True, applies a circular shift to each frame of half its length, prior to computing the FFT
+
+    zero_pad : int
+        For FFT. Number of zeros padded to the window to increase the fft size
+
+    Returns
+    -------
+    X : np.ndarray [shape=(K, M + 1)], complex-valued
+        The discrete short-time Fourier transform
+
+    f : np.ndarray [shape=(K, )], real-valued
+        Center frequencies of all Fourier bins given in Hertz
+
+    t : np.ndarray [shape=(M+1, )], real-valued
+        Time instances where the respective Fourier spectra were computed, given in seconds
+
+    """
+    # Pre-calculations
+    window = win(win_length, win_beta)
+
+    # Zero-pad the window
+    w = np.concatenate((np.zeros(int(np.floor(zero_pad / 2))), window, np.zeros(int(np.floor(zero_pad / 2)))))
+    win_len = int(len(w))
+    win_len_half = np.around(win_len / 2).astype(int)
+
+    max_ana_hop = int(np.max(ana_hop))
+
+    if len(x.shape) == 1:
+        x = x.reshape(-1, 1)
+
+    # Pad the audio to center the windows and to avoid problems at the end
+    x_padded = np.vstack((np.zeros((win_len_half, 1)), x, np.zeros((win_len+max_ana_hop+1, 1))))
+
+    # In case ana_hop is a scalar, sample the window positions evenly in the input signal
+    if np.isscalar(ana_hop):
+        if num_of_frames < 0:
+            num_of_frames = int(np.floor((len(x_padded) - win_len)/ana_hop + 1))
+
+        win_pos = np.arange(num_of_frames).astype(int) * ana_hop
+    else:
+        if num_of_frames < 0:
+            num_of_frames = len(ana_hop)
+
+        win_pos = ana_hop[0:num_of_frames].astype(int)
+
+    # Spectrogram calculation
+    spec = np.zeros((win_len_half + 1, num_of_frames), dtype=complex)
+
+    for i in range(num_of_frames):
+        xi = x_padded[win_pos[i]:win_pos[i] + win_len] * w.reshape(-1, 1)
+
+        if fft_shift == 1:
+            xi = np.fft.fftshift(xi)
+
+        Xi = np.fft.fft(xi, axis=0)
+
+        spec[:, i] = Xi[0:win_len_half + 1, 0]
+
+    # Axis calculation
+    t = win_pos / Fs
+    f = np.arange(0, win_len_half + 1) * Fs / win_len
+
+    return spec, f, t

+
+
+
[docs]def istft(spec, syn_hop=2048, win_length=4096, win_beta=2, Fs=22050, zero_pad=0, num_of_iter=1, orig_sig_len=-1,
+          restore_energy=False, fft_shift=False) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """
+    Computes the 'inverse' Short Time Fourier Transform, according to the paper "Signal Estimation from Modified
+    Short-Time Fourier Transform" by Griffin and Lim.
+
+    Parameters
+    ----------
+    spec : np.ndarray [shape=(K,M+1)] , complex-valued
+        A complex spectrogram generated by STFT.
+
+    syn_hop : int
+        hop size of the synthesis window
+
+    win_length : int
+        length of synthesis window for ISTFT
+
+    win_beta : int
+        exponent of sin^beta window
+
+    Fs : int
+        sampling rate
+
+    zero_pad : int
+        For IFFT. Number of zeros padded to the window to increase the fft size
+
+    num_of_iter : int
+        number of iterations for synthesis
+
+    orig_sig_len : int
+        Original length of the audio signal such that the output can be trimmed accordingly, in samples
+
+    restore_energy : bool
+        For IFFT. When True, rescales every windowed synthesis frame to compensate for synthesis energy leakage
+
+    fft_shift : bool
+        For IFFT. When True, applies a circular shift to each frame of half its length, prior to computing the FFT
+
+    Returns
+    -------
+    y : np.ndarray [shape=(L,1)], real - valued
+        The time-domain signal.
+    """
+
+    # Pre-calculations
+    num_of_frames = spec.shape[1]
+
+    # First iteration
+    Y_i = spec
+    y_i = lsee_mstft(Y_i, syn_hop=syn_hop, win_length=win_length, win_beta=win_beta, zero_pad=zero_pad,
+                     restore_energy=restore_energy, fft_shift=fft_shift)
+
+    # Remaining iterations
+    for j in range(1, num_of_iter):
+        Y_i = np.abs(spec) * np.exp(1j*np.angle(stft(y_i, ana_hop=syn_hop, win_length=win_length, win_beta=win_beta,
+                                                     Fs=Fs, num_of_frames=num_of_frames, fft_shift=fft_shift,
+                                                     zero_pad=zero_pad)[0]))
+        y_i = lsee_mstft(Y_i, syn_hop=syn_hop, win_length=win_length, win_beta=win_beta, zero_pad=zero_pad,
+                         restore_energy=restore_energy, fft_shift=fft_shift)
+
+    y = y_i
+
+    # If the original Length of the signal is known, also remove the zero padding at the end
+    if orig_sig_len > 0:
+        y = y[:orig_sig_len]
+
+    return y

+
+
+
[docs]def lsee_mstft(X, syn_hop=2048, win_length=4096, win_beta=2, zero_pad=0, restore_energy=0, fft_shift=0) -> np.ndarray:
+    """
+    Computes the 'inverse' Short Time Fourier Transform (ISTFT) using the Griffin Lim procedure.
+
+    Parameters
+    ----------
+    X : np.ndarray [shape=(K,M+1)] , complex-valued
+        A complex spectrogram generated by STFT.
+
+    syn_hop : int
+        hop size of the synthesis window
+
+    win_length : int
+        length of analysis and synthesis window for STFT
+
+    win_beta : int
+        exponent of sin^beta window
+
+    zero_pad : int
+        For IFFT. Number of zeros padded to the window to increase the fft size
+
+    restore_energy : bool
+        For IFFT. When True, rescales every windowed synthesis frame to compensate for synthesis energy leakage
+
+    fft_shift : bool
+        For IFFT. When True, applies a circular shift to each frame of half its length, prior to computing the FFT
+
+    Returns
+    -------
+    x: np.ndarray [shape=(L,1)], real-valued
+        The time-domain signal.
+    """
+
+    # Pre-calculations
+    window = win(win_length, win_beta)
+    w = np.concatenate((np.zeros(int(np.floor(zero_pad / 2))), window, np.zeros(int(np.floor(zero_pad / 2)))))
+    win_len = int(len(w))
+    win_len_half = np.around(win_len / 2).astype(int)
+    num_of_frames = X.shape[1]
+    win_pos = np.arange(0, num_of_frames).astype(int) * syn_hop
+    signal_length = win_pos[-1] + win_len
+
+    x = np.zeros((signal_length, 1))  # re-synthesized signal
+    ow = np.zeros((signal_length, 1))  # sum of overlapping windows
+
+    for i in range(num_of_frames):
+        curr_spec = X[:, i]
+
+        # add the conjugate complex symmetric upper half of the spectrum
+        Xi = np.concatenate((curr_spec, np.conj(curr_spec[-2:0:-1])))
+        xi = np.real(np.fft.ifft(Xi, axis=0))
+
+        if fft_shift == 1:
+            xi = np.fft.fftshift(xi)
+
+        xiw = xi * w
+
+        if restore_energy == 1:
+            xi_energy = np.sum(np.abs(xi))
+            xi_w_energy = np.sum(np.abs(xiw))
+            xiw = xiw * (xi_energy/(xi_w_energy+np.finfo(float).eps))
+
+        x[win_pos[i]:win_pos[i] + win_len, 0] += xiw
+        ow[win_pos[i]:win_pos[i] + win_len, 0] += w**2
+
+    ow[ow < 10**-3] = 1  # avoid potential division by zero
+    x = x / ow
+
+    # knowing the zeropads that were added in the stft computation, we can remove them again now. But since we do not
+    # know exactly how many zeros were padded at the end of the signal, it is only safe to remove winLenHalf zeros.
+    x = x[win_len_half:-win_len_half, :]
+
+    return x

+
+
+
[docs]def find_peaks(X) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """
+    Finds peaks on spectrum X. An index in X is considered a peak if its value is the largest among its four nearest
+    neighbours.
+
+    Parameters
+    ----------
+    X : np.ndarray [shape=(K, )] , complex-valued
+        An FFT vector.
+
+    Returns
+    -------
+    peaks : np.ndarray [shape=(P, )] , real-valued
+        Vector with P peaks found
+    """
+    mag_spec = np.abs(X)
+    mag_spec_padded = np.hstack((np.zeros(2), mag_spec, np.zeros(2)))
+    peaks = np.where((mag_spec_padded[4:] < mag_spec_padded[2:-2]) &
+                     (mag_spec_padded[3:-1] < mag_spec_padded[2:-2]) &
+                     (mag_spec_padded[1:-3] < mag_spec_padded[2:-2]) &
+                     (mag_spec_padded[0:-4] < mag_spec_padded[2:-2]))[0]
+
+    infl_region_start = np.zeros(peaks.shape, dtype=int)
+    infl_region_end = np.zeros(peaks.shape, dtype=int)
+
+    if peaks.size == 0:
+        return peaks, infl_region_start, infl_region_end
+
+    infl_region_start[0] = 0
+    infl_region_start[1:] = np.ceil((peaks[1:] + peaks[0:-1])/2)
+    infl_region_end[0:-1] = infl_region_start[1:]
+    infl_region_end[-1] = len(infl_region_end)
+
+    return peaks, infl_region_start, infl_region_end

+
+
+
[docs]def cross_corr(x, y, win_len) -> np.ndarray:
+    """
+    Computes cross correlation between signals x and y over a window of size win_len.
+
+    Parameters
+    ----------
+    x : np.ndarray [shape=(N, )], real or complex - valued
+        Signal to be cross-correlated
+
+    y : np.ndarray [shape=(N, )], real or complex - valued
+        Signal to be cross-correlated
+
+    win_len : int
+        Cross correlation window, in samples
+
+    Returns
+    -------
+    y : np.ndarray [shape=(2N+1,)], real - valued
+        Crosscorrelated signal
+    """
+    # cross correlation is essentially the same as convolution with the first signal being reverted. In principle, we
+    # also need to take the complex conjugate of the reversed x, but since audio signals are real valued, we can skip
+    # this operation.
+    cc = np.convolve(np.flip(x), y)
+
+    # restrict the cross correlation result to just the relevant values
+    # Values outside of this range are related to deltas bigger or smaller than our tolerance values.
+    cc = cc[win_len-1:-(win_len-1)]
+
+    return cc

+
+
+
[docs]def normalize_length(xf, length) -> np.ndarray:
+    """
+    Adjusts the length of signal xf to variable "length".
+
+    Parameters
+    ----------
+    xf : np.ndarray [shape=(N, )], real or complex - valued
+        Signal to be processed
+
+    length : int
+        Signal to be cross-correlated
+
+    Returns
+    -------
+    y : np.ndarray [shape=(length, )], real or complex - valued
+        Signal with modified length
+    """
+
+    if len(xf[:, 0]) < length:
+        pad_len = length - len(xf[:, 0])
+        y = np.concatenate((xf, np.zeros((pad_len, xf.shape[1]))), axis=0)
+    else:
+        y = xf[0:length, :]
+    return y

+
+
+
[docs]def modify_spectral_envelope(x, y, ana_hop=64, win_length=1024, win_beta=1, Fs=22050,  filt_len=24) -> np.ndarray:
+    """
+    Complement to the pitch shifting algorithm, that modifies the formants of the
+    pitch-shifted signal to match them with those of the original signal.
+
+    Parameters
+    ----------
+    x : np.ndarray [shape=(N, )], real - valued
+        Original input signal
+
+    y : np.ndarray [shape=(N, )], real - valued
+        Pitch-shifted signal
+
+    ana_hop : int
+        hop size of the STFT analysis and synthesis window
+
+    win_length : int
+        length of the analysis and synthesis window for STFT
+
+    win_beta : int
+        exponent of sin^beta window
+
+    Fs : int
+        Sampling rate of audio signals x and y
+
+    filt_len : int
+        number of samples for envelope modifying function
+
+    Returns
+    -------
+    y_spec_env_X : np.ndarray [shape=(N,)], real - valued
+        Pitch-shifted signal with modified spectral envelope
+    """
+
+    if len(x.shape) == 1:
+        x = x.reshape(-1, 1)
+
+    if len(y.shape) == 1:
+        y = y.reshape(-1, 1)
+
+    num_of_chan = x.shape[1]
+    y_spec_env_x = np.zeros(y.shape)
+
+    for c in range(num_of_chan):
+        x_c = x[:, c]
+        y_c = y[:, c]
+
+        # stft
+        X, _, _ = stft(x_c, ana_hop=ana_hop, win_length=win_length, win_beta=win_beta, Fs=Fs, num_of_frames=-1,
+                       fft_shift=False, zero_pad=0)
+        Y, _, _ = stft(y_c, ana_hop=ana_hop, win_length=win_length, win_beta=win_beta, Fs=Fs, num_of_frames=-1,
+                       fft_shift=False, zero_pad=0)
+
+        # Compute spectral envelopes
+        env_X = comp_env(X, filt_len)
+        env_Y = comp_env(Y, filt_len)
+        Y_spec_env_X = np.multiply(np.divide(Y, env_Y), env_X)
+
+        # istft
+        y_spec_env_x[:, c] = istft(Y_spec_env_X, syn_hop=ana_hop, win_length=win_length, win_beta=win_beta, Fs=Fs,
+                                   zero_pad=0, num_of_iter=1, orig_sig_len=len(x), restore_energy=False,
+                                   fft_shift=False)[:, 0]
+
+    return y_spec_env_x

+
+
+
[docs]def comp_env(X, filt_len) -> np.ndarray:
+    """
+    Computes the envelope of a given signal spectrum.
+
+    Parameters
+    ----------
+    X : np.ndarray [shape=(K,M+1)] , complex-valued
+        A complex spectrogram
+
+    filt_len : int
+        Length of the convolution window
+
+    Returns
+    -------
+    env : np.ndarray [shape=(K,M+1)] , real-valued
+        Spectral Envelope
+    """
+    kern = win(filt_len, 2)  # Hann Window
+    kern.shape = (-1, 1)  # Turn the window into a 2d array
+    env = sc.signal.convolve2d(np.abs(X), kern, mode='same')  # not equivalent to Matlab's conv2()
+    env = np.divide(env, np.finfo(float).eps + np.tile(np.max(env, axis=0), (env.shape[0], 1)))  # Normalization
+    env = np.multiply(env, np.tile(np.max(np.abs(X), axis=0), (np.abs(X).shape[0], 1)))  # Scaling
+    env[env < 0.01] = 0.01
+
+    return env

+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/_sources/getting_started.rst.txt b/docs/build/html/_sources/getting_started.rst.txt new file mode 100644 index 0000000..6a04d45 --- /dev/null +++ b/docs/build/html/_sources/getting_started.rst.txt @@ -0,0 +1,22 @@ +Getting Started +=============== + +Installation +------------ + +With Python >= 3.6, you can install ``libtsm`` using the Python package manager pip: + +.. code-block:: bash + + pip install libtsm + +Contributing +------------ + +For development, testing, or generating the API documentation, clone the git repository and install: + +.. code-block:: bash + + git clone https://github.com/meinardmueller/libtsm.git + cd libtsm + pip install -e .[dev,tests,docs] diff --git a/docs/build/html/_sources/index.rst.txt b/docs/build/html/_sources/index.rst.txt new file mode 100644 index 0000000..76ba44a --- /dev/null +++ b/docs/build/html/_sources/index.rst.txt @@ -0,0 +1,42 @@ +.. libtsm documentation master file, created by + sphinx-quickstart on Thu May 27 14:48:45 2021. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +libtsm +================================== +``libtsm`` is a Python toolbox for time-scale modification (TSM) and pitch-shifting. + +Details and example application: + +https://www.audiolabs-erlangen.de/resources/MIR/2021-DAFX-AdaptivePitchShifting + +The toolbox is based on a re-implementation of the +`Matlab TSM toolbox `_ by Jonathan Driedger and Meinard Müller. + +If you use this toolbox, please consider the following references: + +.. [#] Sebastian Rosenzweig, Simon Schwär, Jonathan Driedger, and Meinard Müller: Adaptive Pitch-Shifting with Applications to Intonation Adjustment in A Cappella Recordings, Proceedings of the International Conference on Digital Audio Effects (DAFx), 2021. + +.. [#] Jonathan Driedger and Meinard Müller: TSM Toolbox: MATLAB Implementations of Time-Scale Modification Algorithms. In Proceedings of the International Conference on Digital Audio Effects (DAFx): 249–256, 2014. + +.. [#] Jonathan Driedger and Meinard Müller: A Review on Time-Scale Modification of Music Signals. Applied Sciences, 6(2): 57–82, 2016. + +.. [#] Jonathan Driedger, Meinard Müller, and Sebastian Ewert: Improving Time-Scale Modification of Music Signals using Harmonic-Percussive Separation. IEEE Signal Processing Letters, 21(1): 105–109, 2014. + + +.. toctree:: + :hidden: + + getting_started + + + +.. toctree:: + :caption: API Documentation + :maxdepth: 1 + :hidden: + + index_tsm + index_pitchshift + index_utils diff --git a/docs/build/html/_sources/index_pitchshift.rst.txt b/docs/build/html/_sources/index_pitchshift.rst.txt new file mode 100644 index 0000000..fe47b57 --- /dev/null +++ b/docs/build/html/_sources/index_pitchshift.rst.txt @@ -0,0 +1,8 @@ +Pitch-Shifting +============== + +Functions for Pitch-Shifting + +.. automodule:: libtsm.pitchshift + :members: + :undoc-members: diff --git a/docs/build/html/_sources/index_tsm.rst.txt b/docs/build/html/_sources/index_tsm.rst.txt new file mode 100644 index 0000000..a3a2732 --- /dev/null +++ b/docs/build/html/_sources/index_tsm.rst.txt @@ -0,0 +1,8 @@ +Time-Scale Modification +======================= + +Functions for Time-Scale Modification + +.. automodule:: libtsm.tsm + :members: + :undoc-members: diff --git a/docs/build/html/_sources/index_utils.rst.txt b/docs/build/html/_sources/index_utils.rst.txt new file mode 100644 index 0000000..61aa3a2 --- /dev/null +++ b/docs/build/html/_sources/index_utils.rst.txt @@ -0,0 +1,8 @@ +Utils +===== + +Utility Functions + +.. automodule:: libtsm.utils + :members: + :undoc-members: diff --git a/docs/build/html/_static/basic.css b/docs/build/html/_static/basic.css new file mode 100644 index 0000000..cfc60b8 --- /dev/null +++ b/docs/build/html/_static/basic.css @@ -0,0 +1,921 @@ +/* + * basic.css + * ~~~~~~~~~ + * + * Sphinx stylesheet -- basic theme. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +/* -- main layout ----------------------------------------------------------- */ + +div.clearer { + clear: both; +} + +div.section::after { + display: block; + content: ''; + clear: left; +} + +/* -- relbar ---------------------------------------------------------------- */ + +div.related { + width: 100%; + font-size: 90%; +} + +div.related h3 { + display: none; +} + +div.related ul { + margin: 0; + padding: 0 0 0 10px; + list-style: none; +} + +div.related li { + display: inline; +} + +div.related li.right { + float: right; + margin-right: 5px; +} + +/* -- sidebar --------------------------------------------------------------- */ + +div.sphinxsidebarwrapper { + padding: 10px 5px 0 10px; +} + +div.sphinxsidebar { + float: left; + width: 230px; + margin-left: -100%; + font-size: 90%; + word-wrap: break-word; + overflow-wrap : break-word; +} + +div.sphinxsidebar ul { + list-style: none; +} + +div.sphinxsidebar ul ul, +div.sphinxsidebar ul.want-points { + margin-left: 20px; + list-style: square; +} + +div.sphinxsidebar ul ul { + margin-top: 0; + margin-bottom: 0; +} + +div.sphinxsidebar form { + margin-top: 10px; +} + +div.sphinxsidebar input { + border: 1px solid #98dbcc; + font-family: sans-serif; + font-size: 1em; +} + +div.sphinxsidebar #searchbox form.search { + overflow: hidden; +} + +div.sphinxsidebar #searchbox input[type="text"] { + float: left; + width: 80%; + padding: 0.25em; + box-sizing: border-box; +} + +div.sphinxsidebar #searchbox input[type="submit"] { + float: left; + width: 20%; + border-left: none; + padding: 0.25em; + box-sizing: border-box; +} + + +img { + border: 0; + max-width: 100%; +} + +/* -- search page ----------------------------------------------------------- */ + +ul.search { + margin: 10px 0 0 20px; + padding: 0; +} + +ul.search li { + padding: 5px 0 5px 20px; + background-image: url(file.png); + background-repeat: no-repeat; + background-position: 0 7px; +} + +ul.search li a { + font-weight: bold; +} + +ul.search li p.context { + color: #888; + margin: 2px 0 0 30px; + text-align: left; +} + +ul.keywordmatches li.goodmatch a { + font-weight: bold; +} + +/* -- index page ------------------------------------------------------------ */ + +table.contentstable { + width: 90%; + margin-left: auto; + margin-right: auto; +} + +table.contentstable p.biglink { + line-height: 150%; +} + +a.biglink { + font-size: 1.3em; +} + +span.linkdescr { + font-style: italic; + padding-top: 5px; + font-size: 90%; +} + +/* -- general index --------------------------------------------------------- */ + +table.indextable { + width: 100%; +} + +table.indextable td { + text-align: left; + vertical-align: top; +} + +table.indextable ul { + margin-top: 0; + margin-bottom: 0; + list-style-type: none; +} + +table.indextable > tbody > tr > td > ul { + padding-left: 0em; +} + +table.indextable tr.pcap { + height: 10px; +} + +table.indextable tr.cap { + margin-top: 10px; + background-color: #f2f2f2; +} + +img.toggler { + margin-right: 3px; + margin-top: 3px; + cursor: pointer; +} + +div.modindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +div.genindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +/* -- domain module index --------------------------------------------------- */ + +table.modindextable td { + padding: 2px; + border-collapse: collapse; +} + +/* -- general body styles --------------------------------------------------- */ + +div.body { + min-width: 360px; + max-width: 800px; +} + +div.body p, div.body dd, div.body li, div.body blockquote { + -moz-hyphens: auto; + -ms-hyphens: auto; + -webkit-hyphens: auto; + hyphens: auto; +} + +a.headerlink { + visibility: hidden; +} + +h1:hover > a.headerlink, +h2:hover > a.headerlink, +h3:hover > a.headerlink, +h4:hover > a.headerlink, +h5:hover > a.headerlink, +h6:hover > a.headerlink, +dt:hover > a.headerlink, +caption:hover > a.headerlink, +p.caption:hover > a.headerlink, +div.code-block-caption:hover > a.headerlink { + visibility: visible; +} + +div.body p.caption { + text-align: inherit; +} + +div.body td { + text-align: left; +} + +.first { + margin-top: 0 !important; +} + +p.rubric { + margin-top: 30px; + font-weight: bold; +} + +img.align-left, figure.align-left, .figure.align-left, object.align-left { + clear: left; + float: left; + margin-right: 1em; +} + +img.align-right, figure.align-right, .figure.align-right, object.align-right { + clear: right; + float: right; + margin-left: 1em; +} + +img.align-center, figure.align-center, .figure.align-center, object.align-center { + display: block; + margin-left: auto; + margin-right: auto; +} + +img.align-default, figure.align-default, .figure.align-default { + display: block; + margin-left: auto; + margin-right: auto; +} + +.align-left { + text-align: left; +} + +.align-center { + text-align: center; +} + +.align-default { + text-align: center; +} + +.align-right { + text-align: right; +} + +/* -- sidebars -------------------------------------------------------------- */ + +div.sidebar, +aside.sidebar { + margin: 0 0 0.5em 1em; + border: 1px solid #ddb; + padding: 7px; + background-color: #ffe; + width: 40%; + float: right; + clear: right; + overflow-x: auto; +} + +p.sidebar-title { + font-weight: bold; +} + +nav.contents, +aside.topic, +div.admonition, div.topic, blockquote { + clear: left; +} + +/* -- topics ---------------------------------------------------------------- */ + +nav.contents, +aside.topic, +div.topic { + border: 1px solid #ccc; + padding: 7px; + margin: 10px 0 10px 0; +} + +p.topic-title { + font-size: 1.1em; + font-weight: bold; + margin-top: 10px; +} + +/* -- admonitions ----------------------------------------------------------- */ + +div.admonition { + margin-top: 10px; + margin-bottom: 10px; + padding: 7px; +} + +div.admonition dt { + font-weight: bold; +} + +p.admonition-title { + margin: 0px 10px 5px 0px; + font-weight: bold; +} + +div.body p.centered { + text-align: center; + margin-top: 25px; +} + +/* -- content of sidebars/topics/admonitions -------------------------------- */ + +div.sidebar > :last-child, +aside.sidebar > :last-child, +nav.contents > :last-child, +aside.topic > :last-child, +div.topic > :last-child, +div.admonition > :last-child { + margin-bottom: 0; +} + +div.sidebar::after, +aside.sidebar::after, +nav.contents::after, +aside.topic::after, +div.topic::after, +div.admonition::after, +blockquote::after { + display: block; + content: ''; + clear: both; +} + +/* -- tables ---------------------------------------------------------------- */ + +table.docutils { + margin-top: 10px; + margin-bottom: 10px; + border: 0; + border-collapse: collapse; +} + +table.align-center { + margin-left: auto; + margin-right: auto; +} + +table.align-default { + margin-left: auto; + margin-right: auto; +} + +table caption span.caption-number { + font-style: italic; +} + +table caption span.caption-text { +} + +table.docutils td, table.docutils th { + padding: 1px 8px 1px 5px; + border-top: 0; + border-left: 0; + border-right: 0; + border-bottom: 1px solid #aaa; +} + +th { + text-align: left; + padding-right: 5px; +} + +table.citation { + border-left: solid 1px gray; + margin-left: 1px; +} + +table.citation td { + border-bottom: none; +} + +th > :first-child, +td > :first-child { + margin-top: 0px; +} + +th > :last-child, +td > :last-child { + margin-bottom: 0px; +} + +/* -- figures --------------------------------------------------------------- */ + +div.figure, figure { + margin: 0.5em; + padding: 0.5em; +} + +div.figure p.caption, figcaption { + padding: 0.3em; +} + +div.figure p.caption span.caption-number, +figcaption span.caption-number { + font-style: italic; +} + +div.figure p.caption span.caption-text, +figcaption span.caption-text { +} + +/* -- field list styles ----------------------------------------------------- */ + +table.field-list td, table.field-list th { + border: 0 !important; +} + +.field-list ul { + margin: 0; + padding-left: 1em; +} + +.field-list p { + margin: 0; +} + +.field-name { + -moz-hyphens: manual; + -ms-hyphens: manual; + -webkit-hyphens: manual; + hyphens: manual; +} + +/* -- hlist styles ---------------------------------------------------------- */ + +table.hlist { + margin: 1em 0; +} + +table.hlist td { + vertical-align: top; +} + +/* -- object description styles --------------------------------------------- */ + +.sig { + font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; +} + +.sig-name, code.descname { + background-color: transparent; + font-weight: bold; +} + +.sig-name { + font-size: 1.1em; +} + +code.descname { + font-size: 1.2em; +} + +.sig-prename, code.descclassname { + background-color: transparent; +} + +.optional { + font-size: 1.3em; +} + +.sig-paren { + font-size: larger; +} + +.sig-param.n { + font-style: italic; +} + +/* C++ specific styling */ + +.sig-inline.c-texpr, +.sig-inline.cpp-texpr { + font-family: unset; +} + +.sig.c .k, .sig.c .kt, +.sig.cpp .k, .sig.cpp .kt { + color: #0033B3; +} + +.sig.c .m, +.sig.cpp .m { + color: #1750EB; +} + +.sig.c .s, .sig.c .sc, +.sig.cpp .s, .sig.cpp .sc { + color: 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font-style: italic } /* Literal.String.Interpol */ +.highlight .sx { color: #c65d09 } /* Literal.String.Other */ +.highlight .sr { color: #235388 } /* Literal.String.Regex */ +.highlight .s1 { color: #4070a0 } /* Literal.String.Single */ +.highlight .ss { color: #517918 } /* Literal.String.Symbol */ +.highlight .bp { color: #007020 } /* Name.Builtin.Pseudo */ +.highlight .fm { color: #06287e } /* Name.Function.Magic */ +.highlight .vc { color: #bb60d5 } /* Name.Variable.Class */ +.highlight .vg { color: #bb60d5 } /* Name.Variable.Global */ +.highlight .vi { color: #bb60d5 } /* Name.Variable.Instance */ +.highlight .vm { color: #bb60d5 } /* Name.Variable.Magic */ +.highlight .il { color: #208050 } /* Literal.Number.Integer.Long */ \ No newline at end of file diff --git a/docs/build/html/_static/searchtools.js b/docs/build/html/_static/searchtools.js new file mode 100644 index 0000000..97d56a7 --- /dev/null +++ b/docs/build/html/_static/searchtools.js @@ -0,0 +1,566 @@ +/* + * searchtools.js + * ~~~~~~~~~~~~~~~~ + * + * Sphinx JavaScript utilities for the full-text search. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = docUrlRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = docUrlRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms) + ); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + `Search finished, found ${resultCount} page(s) matching the search query.` + ); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() }); + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent !== undefined) return docContent.textContent; + console.warn( + "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + /** + * execute search (requires search index to be loaded) + */ + query: (query) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + // array of [docname, title, anchor, descr, score, filename] + let results = []; + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + results.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id] of foundEntries) { + let score = Math.round(100 * queryLower.length / entry.length) + results.push([ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // lookup as object + objectTerms.forEach((term) => + results.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + results.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item))); + + // now sort the results by score (in opposite order of appearance, since the + // display function below uses pop() to retrieve items) and then + // alphabetically + results.sort((a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; + }); + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + results = results.reverse(); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord) && !terms[word]) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord) && !titleTerms[word]) + arr.push({ files: titleTerms[word], score: Scorer.partialTitle }); + }); + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1) + fileMap.get(file).push(word); + else fileMap.set(file, [word]); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords) => { + const text = Search.htmlToText(htmlText); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/docs/build/html/_static/sphinx_highlight.js b/docs/build/html/_static/sphinx_highlight.js new file mode 100644 index 0000000..aae669d --- /dev/null +++ b/docs/build/html/_static/sphinx_highlight.js @@ -0,0 +1,144 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + parent.insertBefore( + span, + parent.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

' + + '' + + _("Hide Search Matches") + + "

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(SphinxHighlight.highlightSearchWords); +_ready(SphinxHighlight.initEscapeListener); diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html new file mode 100644 index 0000000..e9c7d4f --- /dev/null +++ b/docs/build/html/genindex.html @@ -0,0 +1,278 @@ + + + + + + Index — libtsm 1.1.1 documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Index
    • +
  • +
    • +
+
+
+
+
+ + +

Index

+ +
+ C + | E + | F + | H + | I + | L + | M + | N + | P + | S + | T + | W + +
+

C

+ + + +
+ +

E

+ + +
+ +

F

+ + +
+ +

H

+ + + +
+ +

I

+ + +
+ +

L

+ + + +
    +
  • + libtsm.pitchshift + +
    • +
  • + libtsm.tsm + +
    • +
+ +

M

+ + +
+ +

N

+ + +
+ +

P

+ + + +
+ +

S

+ + +
+ +

T

+ + +
+ +

W

+ + + +
+ + + +
+
+
+ +
+ +
+

© Copyright 2021, Sebastian Rosenzweig, Simon Schwär, Jonathan Driedger, Meinard Müller.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/getting_started.html b/docs/build/html/getting_started.html new file mode 100644 index 0000000..ea93257 --- /dev/null +++ b/docs/build/html/getting_started.html @@ -0,0 +1,138 @@ + + + + + + + Getting Started — libtsm 1.1.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Getting Started

+
+

Installation

+

With Python >= 3.6, you can install libtsm using the Python package manager pip:

+
pip install libtsm
+
+
+
+
+

Contributing

+

For development, testing, or generating the API documentation, clone the git repository and install:

+
git clone https://github.com/meinardmueller/libtsm.git
+cd libtsm
+pip install -e .[dev,tests,docs]
+
+
+
+
+ + +
+
+
+ +
+ +
+

© Copyright 2021, Sebastian Rosenzweig, Simon Schwär, Jonathan Driedger, Meinard Müller.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/index.html b/docs/build/html/index.html new file mode 100644 index 0000000..6adfb83 --- /dev/null +++ b/docs/build/html/index.html @@ -0,0 +1,144 @@ + + + + + + + libtsm — libtsm 1.1.1 documentation + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

libtsm

+

libtsm is a Python toolbox for time-scale modification (TSM) and pitch-shifting.

+

Details and example application:

+

https://www.audiolabs-erlangen.de/resources/MIR/2021-DAFX-AdaptivePitchShifting

+

The toolbox is based on a re-implementation of the +Matlab TSM toolbox by Jonathan Driedger and Meinard Müller.

+

If you use this toolbox, please consider the following references:

+ +
+
+
+
+
+ + +
+
+
+ +
+ +
+

© Copyright 2021, Sebastian Rosenzweig, Simon Schwär, Jonathan Driedger, Meinard Müller.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/index_pitchshift.html b/docs/build/html/index_pitchshift.html new file mode 100644 index 0000000..62e184b --- /dev/null +++ b/docs/build/html/index_pitchshift.html @@ -0,0 +1,172 @@ + + + + + + + Pitch-Shifting — libtsm 1.1.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Pitch-Shifting

+

Functions for Pitch-Shifting

+
+
+libtsm.pitchshift.pitch_shift(x, p, t_p=None, Fs=22050, order='res-tsm', **kwargs) → ndarray[source]
+

(Non-linear) pitch-shifting via time-scale modification and resampling.

+
+
Parameters:
+
    +

  • x (np.ndarray [shape=(N, )], real - valued) – Signal to be transformed

    • +

  • p (float or np.ndarray [shape=(M,)], real - valued) – Amount of pitch shifting to be applied, given in cents. Positive p indicates pitch rising, negative p a pitch +lowering.

    • +

  • t_p (np.ndarray [shape=(M,)], real - valued) – Array of time instances in seconds for adaptive pitch shifting, same length as p. If t==None, a fixed +pitch-shift is assumed.

    • +

  • Fs (int) – Sampling rate of the input audio signal x

    • +

  • order (Order of TSM and resampling, either "res-tsm" or "tsm-res".) –

    • +

  • **kwargs (Parameters for hps_tsm) –

    • +
+
+
Returns:
+

y – The time-scale modified output signal

+
+
Return type:
+

np.ndarray [shape=(L,1)], real - valued

+
+
+
+ +
+
+libtsm.pitchshift.pitch_shift_original(x, n, Fs=22050) → ndarray[source]
+

Pitch modification algorithm via time-scale modification. The input signal is stretched via TSM and then resampled. +The code closely follows the Matlab implementation.

+
+
Parameters:
+
    +

  • x (np.ndarray [shape=(N, )], real - valued) – Signal to be transformed

    • +

  • n (int) – Amount of pitch shifting to be applied, given in cents. Positive n indicates pitch rising, negative n a pitch +lowering

    • +

  • Fs (int) – Sampling rate of the input audio signal x

    • +
+
+
Returns:
+

y – The time-scale modified output signal

+
+
Return type:
+

np.ndarray [shape=(L,1)], real - valued

+
+
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright 2021, Sebastian Rosenzweig, Simon Schwär, Jonathan Driedger, Meinard Müller.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/index_tsm.html b/docs/build/html/index_tsm.html new file mode 100644 index 0000000..6b0ad88 --- /dev/null +++ b/docs/build/html/index_tsm.html @@ -0,0 +1,306 @@ + + + + + + + Time-Scale Modification — libtsm 1.1.1 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Time-Scale Modification

+

Functions for Time-Scale Modification

+
+
+libtsm.tsm.hps_tsm(x, alpha, Fs=22050, hps_ana_hop=256, hps_win_length=1024, hps_win_beta=2, hps_zero_pad=0, hps_fil_len_harm=10, hps_fil_len_perc=10, pv_syn_hop=512, pv_win_length=2048, pv_win_beta=2, pv_zero_pad=0, pv_restore_energy=False, pv_fft_shift=False, ola_syn_hop=128, ola_win_length=256, ola_win_beta=2) → ndarray[source]
+

Time Scale Modification algorithm based on Harmonic - Percussive separation. After separation is +performed, the algorithm uses two phase vocoder TSM and WSOLA TSM algorithms for the Harmonic and percussive part +separately.

+
+
Parameters:
+
    +

  • x (np.ndarray [shape=(N, )], real - valued) – Signal to be transformed

    • +

  • alpha (float or np.ndarray [shape=(S, 2)]) –

    Time stretch function, given by a constant (float) or a set of S anchor points (int). +A valid anchor point sequence

    +
    +
      +

    1. contains only non-negative values,

      2. +

    2. both sequences along the first axis are strictly increasing,

      3. +

    3. starts with position (m, 0), where m is an intereger >= 0.

      4. +
    +
    +

    These conditions will be checked and an error is thrown if they are not met. +See libtsm.ensure_validity for one way to ensure that condition (2) is fulfilled.

    +

    • +

  • Fs (int) – Sampling rate

    • +

  • hps_ana_hop (int) – hop size for HPS

    • +

  • hps_win_length (int) – window length for HPS

    • +

  • hps_win_beta (int) – exponent of sin^beta window

    • +

  • hps_zero_pad (int) – For FFT. Number of zeros padded to the window to increase the fft size

    • +

  • hps_fil_len_harm (int) – Length of the median filter in time direction.

    • +

  • hps_fil_len_perc (int) – Length of the median filter in frequency direction.

    • +

  • pv_syn_hop (int) – hop size for synthesize windows of phase vocoder

    • +

  • pv_win_length (int) – window length for phase vocoder

    • +

  • pv_win_beta (int) – exponent of sin^beta window

    • +

  • pv_zero_pad (int) – phase vocoder zero padding

    • +

  • pv_restore_energy (bool) – restore energy of signal in phase vocoder

    • +

  • pv_fft_shift (bool) – fft shift in phase vocoder

    • +

  • ola_syn_hop (int) – synthesis hop size of OLA

    • +

  • ola_win_length (int) – window length for OLA

    • +

  • ola_win_beta (int) – exponent of sin^beta window

    • +
+
+
Returns:
+

y – The time-scale modified output signal

+
+
Return type:
+

np.ndarray [shape=(L,1)], real - valued

+
+
+
+ +
+
+libtsm.tsm.pv_int_tsm(x, alpha, syn_hop=512, win_length=2048, win_beta=2, Fs=22050, zero_pad=-1, restore_energy=False, fft_shift=True) → ndarray[source]
+

Phase Vocoder Time scale modification algorithm, that rescales the time-axis of the input signal x +according to the time-stretch function s without altering the pitch of x. This algorithm is optimized for integer +values of the time stretching function.

+
+
Parameters:
+
    +

  • x (np.ndarray [shape=(N,)], real - valued) – Signal to be transformed

    • +

  • alpha (int or np.ndarray) – Time stretch factor

    • +

  • syn_hop (int) – hop size of the synthesis window

    • +

  • win_length (int) – length of analysis and synthesis window for STFT

    • +

  • win_beta (int) – exponent of sin^beta window

    • +

  • Fs (int) – Sampling rate of the input audio signal x

    • +

  • zero_pad (int) – For FFT. Number of zeros padded to the window to increase the fft size

    • +

  • restore_energy (bool) – For FFT. When True, rescales every windowed synthesis frame to compensate for synthesis energy leakage

    • +

  • fft_shift (bool) – For FFT. When True, applies a circular shift to each frame of half its length, prior to computing the FFT

    • +
+
+
Returns:
+

y – The time-scale modified output signal

+
+
Return type:
+

np.ndarray [shape=(L,1)], real - valued

+
+
+
+ +
+
+libtsm.tsm.pv_tsm(x, alpha, syn_hop=512, win_length=2048, win_beta=1, Fs=22050, zero_pad=0, restore_energy=False, fft_shift=False, phase_locking=False) → ndarray[source]
+

Time scale modification based on a phase vocoder

+

Rescales the time-axis of the input signal x according to the time-stretch function alpha +without altering the pitch of x.

+
+
Parameters:
+
    +

  • x (np.ndarray [shape=(N, C)], real-valued) – Signal to be transformed, second dimension is an optional channel dimension

    • +

  • alpha (float or np.ndarray [shape=(S, 2)]) –

    Time stretch function, given by a constant (float) or a set of S anchor points (int). +A valid anchor point sequence

    +
    +
      +

    1. contains only non-negative values,

      2. +

    2. both sequences along the first axis are strictly increasing,

      3. +

    3. starts with position (m, 0), where m is an intereger >= 0.

      4. +
    +
    +

    These conditions will be checked and an error is thrown if they are not met. +See libtsm.ensure_validity for one way to ensure that condition (2) is fulfilled.

    +

    • +

  • syn_hop (int) – hop size of the synthesis window

    • +

  • win_length (int) – length of analysis and synthesis window for STFT

    • +

  • win_beta (int) – exponent of sin^beta window

    • +

  • Fs (int) – Sampling rate of the input audio signal x

    • +

  • zero_pad (int) – For FFT. Number of zeros padded to the window to increase the fft size

    • +

  • restore_energy (bool) – For FFT. When True, rescales every windowed synthesis frame to compensate for synthesis energy leakage

    • +

  • fft_shift (bool) – For FFT. When True, applies a circular shift to each frame of half its length, prior to computing the FFT

    • +

  • phase_locking (bool) – when True, Applies identity phase locking

    • +
+
+
Returns:
+

y

+
+
Return type:
+

np.ndarray [shape=(L,1)], real - valued

+
+
+
+ +
+
+libtsm.tsm.two_step_tsm(x, alpha, Fs=22050, order='exact-coarse') → ndarray[source]
+

Time Scale Modification algorithm, where the signal is stretched by the integer and decimal part +of the time stretch function using two different algorithms.

+
+
Parameters:
+
    +

  • x (np.ndarray [shape=(N, )], real - valued) – Signal to be transformed

    • +

  • alpha (float) – Scalar time stretch factor

    • +

  • Fs (int) – Sampling rate of the input audio signal x

    • +

  • order ('exact-coarse' or 'coarse-exact') – Decides which of the two time stretching functions will be computed first, coarse corresponding to the integer +part

    • +
+
+
Returns:
+

y – The time-scale modified output signal

+
+
Return type:
+

np.ndarray [shape=(L,num_of_chan)], real - valued

+
+
+
+ +
+
+libtsm.tsm.wsola_tsm(x, alpha, syn_hop=512, win_length=1024, win_beta=2, tol=512) → ndarray[source]
+

Waveform Similarity Overlap and Add (WSOLA) algorithm that rescales the time-axis of the input signal x +according to the time-stretch function s without altering the pitch of x.

+
+
Parameters:
+
    +

  • x (np.ndarray [shape=(N,num_of_chan)], real - valued) – Signal to be transformed

    • +

  • alpha (float or np.ndarray [shape=(S, 2)]) –

    Time stretch function, given by a constant (float) or a set of S anchor points (int). +A valid anchor point sequence

    +
    +
      +

    1. contains only non-negative values,

      2. +

    2. both sequences along the first axis are strictly increasing,

      3. +

    3. starts with position (m, 0), where m is an intereger >= 0.

      4. +
    +
    +

    These conditions will be checked and an error is thrown if they are not met. +See libtsm.ensure_validity for one way to ensure that condition (2) is fulfilled.

    +

    • +

  • syn_hop (int) – hop size of the synthesis window

    • +

  • win_length (int) – length of the analysis and synthesis window

    • +

  • win_beta (int) – exponent of sin^beta window

    • +

  • tol (int) – Amount of samples the window will be shifted to avoid phase discontinuities when overlap-adding to form the +output signal

    • +
+
+
Returns:
+

y – The time-scale modified output signal

+
+
Return type:
+

np.ndarray [shape=(L,num_of_chan)], real - valued

+
+
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright 2021, Sebastian Rosenzweig, Simon Schwär, Jonathan Driedger, Meinard Müller.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/index_utils.html b/docs/build/html/index_utils.html new file mode 100644 index 0000000..5b3e045 --- /dev/null +++ b/docs/build/html/index_utils.html @@ -0,0 +1,407 @@ + + + + + + + Utils — libtsm 1.1.1 documentation + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Utils

+

Utility Functions

+
+
+libtsm.utils.comp_env(X, filt_len) → ndarray[source]
+

Computes the envelope of a given signal spectrum.

+
+
Parameters:
+
    +

  • X (np.ndarray [shape=(K,M+1)] , complex-valued) – A complex spectrogram

    • +

  • filt_len (int) – Length of the convolution window

    • +
+
+
Returns:
+

env – Spectral Envelope

+
+
Return type:
+

np.ndarray [shape=(K,M+1)] , real-valued

+
+
+
+ +
+
+libtsm.utils.cross_corr(x, y, win_len) → ndarray[source]
+

Computes cross correlation between signals x and y over a window of size win_len.

+
+
Parameters:
+
    +

  • x (np.ndarray [shape=(N, )], real or complex - valued) – Signal to be cross-correlated

    • +

  • y (np.ndarray [shape=(N, )], real or complex - valued) – Signal to be cross-correlated

    • +

  • win_len (int) – Cross correlation window, in samples

    • +
+
+
Returns:
+

y – Crosscorrelated signal

+
+
Return type:
+

np.ndarray [shape=(2N+1,)], real - valued

+
+
+
+ +
+
+libtsm.utils.ensure_validity(alpha, syn_hop=128)[source]
+

Remove points from a sequence of anchor points to ensure a valid TSM input

+

This function removes any anchor points that lead to a non-monotonous increase of the analysis window and +returns a new sequence of anchor points that is a valid TSM input.

+

Note that, depending on the data, this is not a sensible way to ensure a valid TSM input. Often, it may be +advisable to use coarser and semantically meaningful positions for the anchor points.

+
+
Parameters:
+
    +

  • alpha (float or np.ndarray [shape=(S, 2)]) – Time stretch function, given by a set of S anchor points (int).

    • +

  • syn_hop (int) – (smallest) hop size of the synthesis window (default: 128)

    • +
+
+
+
+ +
+
+libtsm.utils.find_peaks(X) → Tuple[ndarray, ndarray, ndarray][source]
+

Finds peaks on spectrum X. An index in X is considered a peak if its value is the largest among its four nearest +neighbours.

+
+
Parameters:
+

X (np.ndarray [shape=(K, )] , complex-valued) – An FFT vector.

+
+
Returns:
+

peaks – Vector with P peaks found

+
+
Return type:
+

np.ndarray [shape=(P, )] , real-valued

+
+
+
+ +
+
+libtsm.utils.hps(x, ana_hop=256, win_length=1024, win_beta=2, Fs=22050, zero_pad=0, fil_len_harm=10, fil_len_perc=10, masking_mode='binary') → Tuple[ndarray, ndarray][source]
+

Harmonic - Percussive separation usign median filters.

+
+
Parameters:
+
    +

  • x (np.ndarray [shape=(N, )], real - valued) – Signal to be transformed

    • +

  • ana_hop (int) – hop size of the synthesis window

    • +

  • win_length (int) – length of analysis and synthesis window for STFT

    • +

  • win_beta (int) – exponent of sin^beta window

    • +

  • Fs (int) – Sampling rate of the input audio signal x

    • +

  • zero_pad (int) – For FFT. Number of zeros padded to the window to increase the fft size

    • +

  • fil_len_harm (int) – Length of the median filter in time direction. A shorter filter makes it more likely +that the signal is interpreted as harmonic.

    • +

  • fil_len_perc (int) – Length of the median filter in frequency direction. A shorter filter makes it more likely +that the signal is interpreted as percussive.

    • +

  • masking_mode (either "binary" or "relative") – Selects Harmonic Percussive separation masking mode (soft or binary masking)

    • +
+
+
Returns:
+

    +

  • x_harm (np.ndarray) – Harmonic Component of input signal x

    • +

  • x_perc (np.ndarray) – Percussive Component of input signal x

    • +
+

+
+
+
+ +
+
+libtsm.utils.istft(spec, syn_hop=2048, win_length=4096, win_beta=2, Fs=22050, zero_pad=0, num_of_iter=1, orig_sig_len=-1, restore_energy=False, fft_shift=False) → Tuple[ndarray, ndarray, ndarray][source]
+

Computes the ‘inverse’ Short Time Fourier Transform, according to the paper “Signal Estimation from Modified +Short-Time Fourier Transform” by Griffin and Lim.

+
+
Parameters:
+
    +

  • spec (np.ndarray [shape=(K,M+1)] , complex-valued) – A complex spectrogram generated by STFT.

    • +

  • syn_hop (int) – hop size of the synthesis window

    • +

  • win_length (int) – length of synthesis window for ISTFT

    • +

  • win_beta (int) – exponent of sin^beta window

    • +

  • Fs (int) – sampling rate

    • +

  • zero_pad (int) – For IFFT. Number of zeros padded to the window to increase the fft size

    • +

  • num_of_iter (int) – number of iterations for synthesis

    • +

  • orig_sig_len (int) – Original length of the audio signal such that the output can be trimmed accordingly, in samples

    • +

  • restore_energy (bool) – For IFFT. When True, rescales every windowed synthesis frame to compensate for synthesis energy leakage

    • +

  • fft_shift (bool) – For IFFT. When True, applies a circular shift to each frame of half its length, prior to computing the FFT

    • +
+
+
Returns:
+

y – The time-domain signal.

+
+
Return type:
+

np.ndarray [shape=(L,1)], real - valued

+
+
+
+ +
+
+libtsm.utils.lsee_mstft(X, syn_hop=2048, win_length=4096, win_beta=2, zero_pad=0, restore_energy=0, fft_shift=0) → ndarray[source]
+

Computes the ‘inverse’ Short Time Fourier Transform (ISTFT) using the Griffin Lim procedure.

+
+
Parameters:
+
    +

  • X (np.ndarray [shape=(K,M+1)] , complex-valued) – A complex spectrogram generated by STFT.

    • +

  • syn_hop (int) – hop size of the synthesis window

    • +

  • win_length (int) – length of analysis and synthesis window for STFT

    • +

  • win_beta (int) – exponent of sin^beta window

    • +

  • zero_pad (int) – For IFFT. Number of zeros padded to the window to increase the fft size

    • +

  • restore_energy (bool) – For IFFT. When True, rescales every windowed synthesis frame to compensate for synthesis energy leakage

    • +

  • fft_shift (bool) – For IFFT. When True, applies a circular shift to each frame of half its length, prior to computing the FFT

    • +
+
+
Returns:
+

x – The time-domain signal.

+
+
Return type:
+

np.ndarray [shape=(L,1)], real-valued

+
+
+
+ +
+
+libtsm.utils.median_filter(X, filt_len, dim) → ndarray[source]
+

Median filter implementation.

+
+
Parameters:
+
    +

  • X (np.ndarray) – Spectrogram

    • +

  • filt_len (int) – Median filter length

    • +

  • dim (int) – Dimension in which median filter should be applied

    • +
+
+
Returns:
+

Y – Median-filtered spectrogram

+
+
Return type:
+

np.ndarray

+
+
+
+ +
+
+libtsm.utils.modify_spectral_envelope(x, y, ana_hop=64, win_length=1024, win_beta=1, Fs=22050, filt_len=24) → ndarray[source]
+

Complement to the pitch shifting algorithm, that modifies the formants of the +pitch-shifted signal to match them with those of the original signal.

+
+
Parameters:
+
    +

  • x (np.ndarray [shape=(N, )], real - valued) – Original input signal

    • +

  • y (np.ndarray [shape=(N, )], real - valued) – Pitch-shifted signal

    • +

  • ana_hop (int) – hop size of the STFT analysis and synthesis window

    • +

  • win_length (int) – length of the analysis and synthesis window for STFT

    • +

  • win_beta (int) – exponent of sin^beta window

    • +

  • Fs (int) – Sampling rate of audio signals x and y

    • +

  • filt_len (int) – number of samples for envelope modifying function

    • +
+
+
Returns:
+

y_spec_env_X – Pitch-shifted signal with modified spectral envelope

+
+
Return type:
+

np.ndarray [shape=(N,)], real - valued

+
+
+
+ +
+
+libtsm.utils.normalize_length(xf, length) → ndarray[source]
+

Adjusts the length of signal xf to variable “length”.

+
+
Parameters:
+
    +

  • xf (np.ndarray [shape=(N, )], real or complex - valued) – Signal to be processed

    • +

  • length (int) – Signal to be cross-correlated

    • +
+
+
Returns:
+

y – Signal with modified length

+
+
Return type:
+

np.ndarray [shape=(length, )], real or complex - valued

+
+
+
+ +
+
+libtsm.utils.stft(x, ana_hop=2048, win_length=4096, win_beta=2, Fs=22050, num_of_frames=-1, fft_shift=False, zero_pad=0) → Tuple[ndarray, ndarray, ndarray][source]
+

Computes the Short-Time Fourier Transform (STFT) of the input audio signal.

+
+
Parameters:
+
    +

  • x (np.ndarray, real-valued) – Signal to be transformed

    • +

  • ana_hop (int or np.ndarray) – hop size of the analysis window

    • +

  • win_length (int) – length of analysis window for STFT

    • +

  • win_beta (int) – exponent of sin^beta window

    • +

  • Fs (int) – Sampling rate of the input audio signal x

    • +

  • num_of_frames (int) – Fixes the number of FFT frames to be computed

    • +

  • fft_shift (bool) – For FFT. When True, applies a circular shift to each frame of half its length, prior to computing the FFT

    • +

  • zero_pad (int) – For FFT. Number of zeros padded to the window to increase the fft size

    • +
+
+
Returns:
+

    +

  • X (np.ndarray [shape=(K, M + 1)], complex-valued) – The discrete short-time Fourier transform

    • +

  • f (np.ndarray [shape=(K, )], real-valued) – Center frequencies of all Fourier bins given in Hertz

    • +

  • t (np.ndarray [shape=(M+1, )], real-valued) – Time instances where the respective Fourier spectra were computed, given in seconds

    • +
+

+
+
+
+ +
+
+libtsm.utils.win(win_len, beta) → ndarray[source]
+

Generates a sin^beta window.

+
+
Parameters:
+
    +

  • win_len (int) – length of the window

    • +

  • beta (int) – Exponent of the window

    • +
+
+
Returns:
+

w – The desired window

+
+
Return type:
+

np.ndarray [shape=(win_len, )]

+
+
+
+ +
+ + +
+
+
+ +
+ +
+

© Copyright 2021, Sebastian Rosenzweig, Simon Schwär, Jonathan Driedger, Meinard Müller.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
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+
+ + + + \ No newline at end of file diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv new file mode 100644 index 0000000..0b04d64 Binary files /dev/null and b/docs/build/html/objects.inv differ diff --git a/docs/build/html/py-modindex.html b/docs/build/html/py-modindex.html new file mode 100644 index 0000000..58590a9 --- /dev/null +++ b/docs/build/html/py-modindex.html @@ -0,0 +1,144 @@ + + + + + + Python Module Index — libtsm 1.1.1 documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Python Module Index
    • +
  • +
    • +
+
+
+
+
+ + +

Python Module Index

+ +
+ l +
+ + + + + + + + + + + + + + + + +
 
+ l
+ libtsm +
    + libtsm.pitchshift +
    + libtsm.tsm +
    + libtsm.utils +
+ + +
+
+
+ +
+ +
+

© Copyright 2021, Sebastian Rosenzweig, Simon Schwär, Jonathan Driedger, Meinard Müller.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/build/html/search.html b/docs/build/html/search.html new file mode 100644 index 0000000..7e0c779 --- /dev/null +++ b/docs/build/html/search.html @@ -0,0 +1,129 @@ + + + + + + Search — libtsm 1.1.1 documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Search
    • +
  • +
    • +
+
+
+
+
+ + + + +
+ +
+ +
+
+
+ +
+ +
+

© Copyright 2021, Sebastian Rosenzweig, Simon Schwär, Jonathan Driedger, Meinard Müller.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + + + + + + \ No newline at end of file diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js new file mode 100644 index 0000000..e37f869 --- /dev/null +++ b/docs/build/html/searchindex.js @@ -0,0 +1 @@ +Search.setIndex({"docnames": ["getting_started", "index", "index_pitchshift", "index_tsm", "index_utils"], "filenames": ["getting_started.rst", "index.rst", "index_pitchshift.rst", "index_tsm.rst", "index_utils.rst"], "titles": ["Getting Started", "libtsm", "Pitch-Shifting", "Time-Scale Modification", "Utils"], "terms": {"With": 0, "python": [0, 1], "3": [0, 1], "6": [0, 1], "you": [0, 1], "can": [0, 4], "libtsm": [0, 2, 3, 4], "us": [0, 1, 3, 4], "packag": 0, "manag": 0, "pip": 0, "For": [0, 3, 4], "develop": 0, "test": 0, "gener": [0, 4], "api": 0, "document": 0, "clone": 0, "git": 0, "repositori": 0, "http": [0, 1], "github": 0, "com": 0, "meinardmuel": 0, "cd": 0, "e": 0, "dev": 0, "doc": 0, "i": [1, 2, 3, 4], "toolbox": 1, "time": [1, 2, 4], "scale": 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