  We propose a data-driven design method of perfect-reconstruction filterbank
(PRFB) for sound-source enhancement (SSE) based on deep neural network (DNN).
DNNs have been used to estimate a time-frequency (T-F) mask in the short-time
Fourier transform (STFT) domain. Their training is more stable when a simple
cost function as mean-squared error (MSE) is utilized comparing to some
advanced cost such as objective sound quality assessments. However, such a
simple cost function inherits strong assumptions on the statistics of the
target and/or noise which is often not satisfied, and the mismatch of
assumption results in degraded performance. In this paper, we propose to design
the frequency scale of PRFB from training data so that the assumption on MSE is
satisfied. For designing the frequency scale, the warped filterbank frame
(WFBF) is considered as PRFB. The frequency characteristic of learned WFBF was
in between STFT and the wavelet transform, and its effectiveness was confirmed
by comparison with a standard STFT-based DNN whose input feature is compressed
into the mel scale.
