  The design of acoustic features is important for speech separation. It can be
roughly categorized into three classes: handcrafted, parameterized, and
learnable features. Among them, learnable features, which are trained with
separation networks jointly in an end-to-end fashion, become a new trend of
modern speech separation research, e.g. convolutional time domain audio
separation network (Conv-Tasnet), while handcrafted and parameterized features
are also shown competitive in very recent studies. However, a systematic
comparison across the three kinds of acoustic features has not been conducted
yet. In this paper, we compare them in the framework of Conv-Tasnet by setting
its encoder and decoder with different acoustic features. We also generalize
the handcrafted multi-phase gammatone filterbank (MPGTF) to a new parameterized
multi-phase gammatone filterbank (ParaMPGTF). Experimental results on the
WSJ0-2mix corpus show that (i) if the decoder is learnable, then setting the
encoder to STFT, MPGTF, ParaMPGTF, and learnable features lead to similar
performance; and (ii) when the pseudo-inverse transforms of STFT, MPGTF, and
ParaMPGTF are used as the decoders, the proposed ParaMPGTF performs better than
the other two handcrafted features.

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