Speech Denoising with Residual Attention U-Net
Jun Takahashi, Sayuri Kohmura, Taro Togawa · 2020
We applied end-to-end speech denoising to remove background noises from noisy, monaural speech signals by directly processing a raw waveform. Recent approaches have demonstrated effective results using various deep neural network (DNN) architectures. We propose the residual attention U-Net, which connects the same layer of multiple stacked residual channel attention encoder/decoder models for speech denoising. We evaluated the proposed method using an unseen test with single-channel speech denoising. Both objective and subjective evaluations indicated that our proposed method is preferred to other speech denoising methods.