MTS

Number of speakers detection

How it works

The baseline is next:

• Voice Activity Detection (VAD) first to identify the speech regions
• Mixing the voices and extracting the MFCC features (13-dimensional) per speech frame
• Classification of sequence of N frames using LSTM and CNN classifiers

Files description:

• code/main.py - main code to detect the number of speakers in an audio file
• code/prepare_training_data.ipynb - vizualisations and data preprocessing, dataset preparation
• code/train_model_LSTM.ipynb - trains LSTM classifier
• code/train_model_CNN.ipynb - trains CNN classifier
• code/vad.py and code/utils/estnoise_ms.py - are borrowed from source [1] - Voice Activity Detector

To install the environment

• conda config --add channels anaconda
• conda config --append channels conda-forge
• conda create -n "name_of_new_environment" --file package-list.txt
• acivate the environment by source activate "name_of_new_environment"
• run pip install python-speech-features

Download and extract dataset

• To download the dataset run in terminal wget --mirror --no-parent http://www.repository.voxforge1.org/downloads/Russian/Trunk/Audio/Main/16kHz_16bit/

• Navigate to the 16kHz_16bit/ directory and run in terminal for i in *.tgz; do echo working on $i; tar xvzf $i ; done to unzip all the archives

• Move folder 16kHz_16bit/ with all the nested folders to the folder data/

To run the code

• Navigate to the folder code/
• Run in terminal python main.py

References

1. https://github.com/eesungkim/Voice_Activity_Detector
2. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7298570
3. https://mycourses.aalto.fi/pluginfile.php/146209/mod_resource/content/1/slides_07_vad.pdf

Python version

Python 3.6.3