i-RevNet-based time-frequency transform for speech enhancement

In this repository, i-RevNet-based nonlinear time-frequency transform for speech enhancement is impremented using Pytorch.

Our paper can be found here. If you use codes in this repository, please cite this paper.

In our paper, VoiceBank-DEMAND dataset [2] (available here) is used.

Dependencies

We have tested these codes on follwoing environment:

• Python 3.6.4
• Pytorch 1.4.0
• NumPy 1.17.2
• CUDA Version 10.1
• cuDNN Version 7501

Usage example

A set of Python codes for training and test are available.

Run "01_train.py" to train a model

Run "02_test.py" to evaluate a model and write .wav files of enhanced speeches

Note that paths in each code need to be changed for your environment.

Reference

[1] D. Takeuchi, K. Yatabe, Y. Koizumi, Y. Oikawa, and N. Harada, “Invertible DNN-based nonlinear time-frequency transform for speech enhancement ,” in 2020 IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), 2020. (accepted)

[2] C. Valentini-Botinho, X. Wang, S. Takaki, and J. Yamagishi, “Investigating RNN-based speech enhancement methods for noise-robust Text-to-Speech.,” in 9th ISCA Speech Synth. Workshop, 2016, pp. 146–152.