Paper Info
Title
Trainable Adaptive Window Switching For Speech Enhancement
Abstract
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.
Year
DOI
Venue
2018
10.1109/icassp.2019.8683642
2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
Keywords
Field
DocType
Speech enhancement, trainable time-frequency representation, adaptive window switching, MDCT
Speech enhancement,Uncertainty principle,Modified discrete cosine transform,Computer science,Short-time Fourier transform,Speech recognition,Fourier transform,Window switching,Window function
Journal
Volume
ISSN
Citations 
abs/1811.02438
1520-6149
0
PageRank 
References 
Authors
0.34
13
3
Name
Order
Citations
PageRank
Koizumi Yuma14111.75
Harada Noboru26725.07
Yoichi Haneda39720.16