Speech Emotion Recognition

This repository contains our work on Speech emotion recognition using emodb dataset. This dataset is available here Emo-db

Prerequisites

Linux (preferable Ubuntu LTS). Python2.x

Installing dependencies

Note: You can skip this step, if you are installing the packages. Dependencies are listed below and in the requirements.txt file.

• h5py
• Keras
• scipy
• sklearn
• speechpy
• tensorflow

Install one of python package managers in your distro. If you install pip, then you can install the dependencies by running pip3 install -r requirements.txt

If you prefer to accelerate keras training on GPU's you can install tensorflow-gpu by pip3 install tensorflow-gpu

Directory Structure

• speechemotionrecognition/ - Package folder which contains all the code files corresponding to package
• dataset/ - Contains the speech files in wav formatted seperated into 7 folders which are the corresponding labels of those files
• models/ - Contains the saved models which obtained best accuracy on test data.
• examples/ - Contains examples on how to use the package

Details of the package

• utilities.py - Contains code to read the files, extract the features and create test and train data
• mlmodel.py - Code to train non DL models. We have three models
  • 1 - SVM
  • 2 - Random Forest
  • 3 - Neural Network
• dnn.py - Code to train Deep learning Models. Supports two models given below
  • 1 - CNN
  • 2 - LSTM

Examples

Have a look at examples/ directory. ml_example.py has examples using ML models. cnn_example.py and lstm_example.py has examples using cnn and lstm models.

Documentation

Code documentation can be found here

Installation

A setup.py file is provided in the repository. You can run sudo python3 setup.py install to install it at system level. If you don't have privileges to do so, you can install it at user level by running python3 setup.py install --user.

Contributing to the repository.

• If you find any problem with the code, please feel free to open an issue.
• Found something you can improve, please send me a pull request with your changes. I will be more than happy to review and approve them.

Note: If you find this code useful, please leave a star :)