WaveCleaver

About

The aim of this project is to build a deep learning system that identifies the human voice and isolates the other sources of sound from a single waveform. The application of this technology is extensive, with potential benefits including improved performance of noise-canceling systems and enhanced quality of audio communications.

Results

Experiment	SDR Metric
Bandpass Filter	7.8
Spectral Gating	7.2
Sepformer	10.4
Conv-TasNet	8.53
Waveform AutoEncoder	-3.1
Spectogram AutoEncoder	13.77

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Usage

Environment Setup

1. Install Conda: If you haven't already, install Conda by following the instructions on the official Conda documentation.

2. Create a New Environment: Open your terminal or command prompt and run the following command to create a new Conda environment. Replace myenv with your desired environment name.

   conda create --name myenv

3. Activate the Environment: Once the environment is created, activate it using the following command:

   conda activate myenv

4. Install Dependencies: Once you have your requirements.txt file ready, you can install all the dependencies listed in it using pip. Run the following command in your terminal:

   pip install -r requirements.txt

Running the experiments

The experiments above were run via notebooks. Simply follow the general setup steps, then run the notebooks top-down.

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Files

• In /data we have all the relevant files for the custom test dataset, our model inferencing results as .csv files, and the .csv files for all of our loss and accuracy curves
• In the /notebooks path we have the notebooks for all of our experimentation.
• Our from scratch model can be found in notebooks/spectogram_autoencoder.ipynb and notebooks/autoencoder.ipynb
• Our non-ml methods can be found in notebooks/Python_Noise_Reduction_Methods.ipynb
• Our experiments for inferencing can be found in notebooks/pretained_sound_separation.ipynb and notebooks/sepformer.ipynb
• Our attempt at transfer learning on demucs can be found in notebook/finetuning-demucs.ipynb