  Short-time Fourier transform (STFT) is used as the front end of many popular
successful monaural speech separation methods, such as deep clustering (DPCL),
permutation invariant training (PIT) and their various variants. Since the
frequency component of STFT is linear, while the frequency distribution of
human auditory system is nonlinear. In this work we propose and give an
empirical study to use an alternative front end called constant Q transform
(CQT) instead of STFT to achieve a better simulation of the frequency resolving
power 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) is
higher than that based on STFT. In the same experimental setting on WSJ0-2mix
corpus, we examined the performance of CQT under different backends, including
the original DPCL, utterance level PIT, and some of their variants. It is found
that all CQT-based methods are better than STFT-based methods, and achieved on
average 0.4dB better performance than STFT based method in SDR improvements.
