Acoustical Science and Technology
Online ISSN : 1347-5177
Print ISSN : 1346-3969
ISSN-L : 0369-4232
TECHNICAL REPORTS
Effect of spectrogram resolution on deep-neural-network-based speech enhancement
Daiki TakeuchiKohei YatabeYuma KoizumiYasuhiro OikawaNoboru Harada
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2020 Volume 41 Issue 5 Pages 769-775

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Abstract

In recent single-channel speech enhancement, deep neural network (DNN) has played a quite important role for achieving high performance. One standard use of DNN is to construct a mask-generating function for time-frequency (T-F) masking. For applying a mask in T-F domain, the short-time Fourier transform (STFT) is usually utilized because of its well-understood and invertible nature. While the mask-generating regression function has been studied for a long time, there is less research on T-F transform from the viewpoint of speech enhancement. Since the performance of speech enhancement depends on both the T-F mask estimator and T-F transform, investigating T-F transform should be beneficial for designing a better enhancement system. In this paper, as a step toward optimal T-F transform in terms of speech enhancement, we experimentally investigated the effect of parameter settings of STFT on a DNN-based mask estimator. We conducted the experiments using three types of DNN architectures with three types of loss functions, and the results suggested that U-Net is robust to the parameter setting while that is not the case for fully connected and BLSTM networks.

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© 2020 by The Acoustical Society of Japan
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