pranoot / speech_separation

Include some core functions and model to handle speech separation

speech_separation

This is a repository for speech separation tasks.

This project is highly inspired by the paper[1], and is still working to improve the performance.

Data

AVspeech dataset : contains 4700 hours of video segments, from a total of 290k YouTube videos.

Customized video and audio downloader are provided in data/. (based on youtube-dl,sox,ffmpeg)

Preprocessing

There are several preprocess functions in the lib. Including STFT, iSTFT, power-law compression, complex mask etc.

Apply MTCNN to detect face and correct it by checking the provided face center. [2]

The visual frames are transfered to 1792 (avg pooling layer) face embeddings with facenet pre-trained model[3].

Model

Audio part : Dilated CNN + Bidirectional LSTM.

Video part : (pretrained MTCNN + Facenet) + dilated CNN + Bidirectional LSTM.

Loss function : modified discriminative loss function inspired from paper[4].

Prediction

Apply complex ratio mask (cRM) to enhance phase spectrum. Maintain the quality during transformation by hyperbolic tangent fucntion.[5]

The model will be evaluated by signal-to-distortion ratio.

Reference

[1] Lookng to Listen at the Cocktail Party:A Speaker-Independent Audio-Visual Model for Speech Separation, A. Ephrat et al., arXiv:1804.03619v2 [cs.SD] 9 Aug 2018

[2] MTCNN face detection

[3] FaceNet Pretrained model

[4] Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source Separation, P. Hunag et al,arXiv:1502.04149v4 [cs.SD] 1 Oct 2015

[5] Complex Ratio Masking for Monaural Speech Separation