  This study proposes a trainable adaptive window switching (AWS) method and
apply it to a deep-neural-network (DNN) for speech enhancement in the modified
discrete cosine transform domain. Time-frequency (T-F) mask processing in the
short-time Fourier transform (STFT)-domain is a typical speech enhancement
method. To recover the target signal precisely, DNN-based short-time frequency
transforms have recently been investigated and used instead of the STFT.
However, since such a fixed-resolution short-time frequency transform method
has a T-F resolution problem based on the uncertainty principle, not only the
short-time frequency transform but also the length of the windowing function
should be optimized. To overcome this problem, we incorporate AWS into the
speech enhancement procedure, and the windowing function of each time-frame is
manipulated using a DNN depending on the input signal. We confirmed that the
proposed method achieved a higher signal-to-distortion ratio than conventional
speech enhancement methods in fixed-resolution frequency domains.
