Neural Cascade Architecture with Triple-domain Loss for Speech Enhancement

This paper proposes a neural cascade architecture to address the monaural speech enhancement problem. The cascade architecture is composed of three modules which optimize in turn enhanced speech with respect to the magnitude spectrogram, the time-domain signal and the complex spectrogram. Each module takes as input the noisy speech and the output obtained from the previous module, and generates a prediction of the respective target. Our model is trained in an end-to-end manner, using a triple-domain loss function that accounts for three domains of signal representation. Experimental results on the WSJ0 SI-84 corpus show that the proposed model outperforms other strong speech enhancement baselines in terms of objective speech quality and intelligibility.