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Short-time Fourier transform (STFT) is used as the front end of many popularsuccessful monaural speech separation methods, such as deep clustering (DPCL),permutation invariant training (PIT) and their various variants. Since thefrequency component of STFT is linear, while the frequency distribution ofhuman auditory system is nonlinear. In this work we propose and give anempirical study to use an alternative front end called constant Q transform(CQT) instead of STFT to achieve a better simulation of the frequency resolvingpower of the human auditory system. The upper bound in signal-to-distortion(SDR) of ideal speech separation based on CQT's ideal ration mask (IRM) ishigher than that based on STFT. In the same experimental setting on WSJ0-2mixcorpus, we examined the performance of CQT under different backends, includingthe original DPCL, utterance level PIT, and some of their variants. It is foundthat all CQT-based methods are better than STFT-based methods, and achieved onaverage 0.4dB better performance than STFT based method in SDR improvements.

Is CQT more suitable for monaural speech separation than STFT? an empirical study