[2203.16222] Phase-Aware Deep Speech Enhancement: It's All About The Frame Length
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Electrical Engineering and Systems Science > Audio and Speech Processing

arXiv:2203.16222 (eess)
[Submitted on 30 Mar 2022 (v1), last revised 4 Oct 2022 (this version, v2)]

Title:Phase-Aware Deep Speech Enhancement: It's All About The Frame Length

Authors:Tal Peer, Timo Gerkmann
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Abstract:Algorithmic latency in speech processing is dominated by the frame length used for Fourier analysis, which in turn limits the achievable performance of magnitude-centric approaches. As previous studies suggest the importance of phase grows with decreasing frame length, this work presents a systematical study on the contribution of phase and magnitude in modern Deep Neural Network (DNN)-based speech enhancement at different frame lengths. Results indicate that DNNs can successfully estimate phase when using short frames, with similar or better overall performance compared to using longer frames. Thus, interestingly, modern phase-aware DNNs allow for low-latency speech enhancement at high quality.
Comments: The following article has been accepted by JASA Express Letters. After it is published, it will be found at this http URL
Subjects: Audio and Speech Processing (eess.AS); Machine Learning (cs.LG); Sound (cs.SD)
Cite as: arXiv:2203.16222 [eess.AS]
  (or arXiv:2203.16222v2 [eess.AS] for this version)
  https://doi.org/10.48550/arXiv.2203.16222
Journal reference: JASA Express Letters 2, 104802 (2022)
Related DOI: https://doi.org/10.1121/10.0014875

Submission history

From: Tal Peer [view email]
[v1] Wed, 30 Mar 2022 11:51:30 UTC (483 KB)
[v2] Tue, 4 Oct 2022 14:59:44 UTC (571 KB)
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