/rad

rad/main.js

@@ -46,68 +46,29 @@ async function redrawRT() {
   let time = Date.now();
   is_drawing = true;
 
+  let draw_sg = (sg) => utils.drawSpectrogram(canvas, sg,
+    { fs_full: true, db_log: s => s ** (1 / conf.brightness) });
+
   try {
     log('decoding audio file:', audio_file.name);
     let audio_signal = await utils.decodeAudioFile(audio_file, conf.sampleRate);
-
-    log('computing spectrogram');
     let spectrogram = await utils.computePaddedSpectrogram(audio_signal, {
       num_frames: conf.numFrames,
       frame_size: conf.frameSize,
     });
-    // await utils.drawSpectrogram(canvas, spectrogram, { fs_full: true });
-    // throw new Error('dbg224');
-
-    log('computing autocorrelation');
     let correlogram = computeAutoCorrelation(spectrogram);
-    // await utils.drawSpectrogram(canvas, correlogram, { fs_full: true });
-    // throw new Error('dbg224');
-
     let diskogram = createDiskSpectrogram(correlogram);
-    // await utils.drawSpectrogram(canvas, diskogram, { fs_full: true });
-    // throw new Error('dbg224');
-
-    log('computing radon transform');
     let radogram = utils.computeFFT2D(diskogram);
-
-    log('drawing radon sinogram');
-    utils.drawSpectrogram(canvas, radogram,
-      { fs_full: true, rgb_fn: s => [s * 25, s * 5, s * 1], db_log: s => s ** (1 / conf.brightness) });
-
+    await draw_sg(radogram);
+  } finally {
     utils.shiftCanvasData(canvas, { dx: canvas.width / 2 });
     utils.shiftCanvasData(canvas, { dy: canvas.height / 2 });
-  } finally {
     is_drawing = false;
   }
 
   log('done in', Date.now() - time, 'ms');
 }
 
-function createSpectrogramFilter(sample_rate, freq_hz_min, freq_hz_max, multiplier = 1.0) {
-  return (f, fs) => {
-    let hz = min(f, fs - f) / fs * sample_rate;
-    return multiplier * utils.hann((hz - freq_hz_min) / (freq_hz_max - freq_hz_min));
-  };
-}
-
-function applySpectrogramFilter(spectrogram, filter_fn) {
-  let [nf, fs] = spectrogram.dimensions;
-  let output = spectrogram.clone();
-
-  for (let t = 0; t < nf; t++) {
-    let src = spectrogram.subtensor(t);
-    let res = output.subtensor(t);
-
-    for (let f = 0; f < fs; f++) {
-      let x = filter_fn(f, fs);
-      res.array[2 * f + 0] = x * src.array[2 * f + 0];
-      res.array[2 * f + 1] = x * src.array[2 * f + 1];
-    }
-  }
-
-  return output;
-}
-
 function computeAutoCorrelation(spectrogram) {
   let output = spectrogram.clone();
   let [nf, fs] = spectrogram.dimensions;
@@ -139,24 +100,21 @@ function createDiskSpectrogram(spectrogram, disk_size) {
   let sqr2 = (x) => x * x;
   let disk = new utils.Float32Tensor([ds, ds, 2]);
 
-  for (let y = -ds / 2; y < ds / 2; y++) {
-    for (let x = -ds / 2; x < ds / 2; x++) {
-      let r = sqrt(x * x + y * y) / ds * 2; // 0..1
-      let a = atan2(y, x) / PI; // -1..1
-      if (r == 0 || r >= 1) continue;
-
-      let t = min(nf - 1, r * nf | 0); // frame id
-      let f = ((abs(a) * fs / 2 | 0) + fs) % fs; // freq id
-      dcheck(t >= 0 && t < nf);
-      dcheck(f >= 0 && f < fs);
+  for (let t = 0; t < nf; t++) {
+    for (let f = -fs / 2; f < fs / 2; f++) {
+      let r = t / nf;
+      let a = f / fs * 2 * Math.PI;
+      let y = r * Math.sin(a) * ds / 2 | 0;
+      let x = r * Math.cos(a) * ds / 2 | 0;
 
       let yx = ((y + ds) % ds * ds + (x + ds) % ds) * 2;
-      let tf = (t * fs + f) * 2;
+      let tf = (t * fs + (f + fs) % fs) * 2;
+      dcheck(yx >= 0 && tf >= 0);
 
       let re = spectrogram.array[tf + 0];
       let im = spectrogram.array[tf + 1];
-      disk.array[yx + 0] = re;
-      disk.array[yx + 1] = im;
+      disk.array[yx + 0] += re; // re*re+im*im;
+      disk.array[yx + 1] += im; // 0;
     }
   }