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A Study on the Benefits of Phase-Aware Speech Enhancement in Challenging Noise Scenarios

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Latent Variable Analysis and Signal Separation (LVA/ICA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10891))

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Abstract

In recent years, there has been a renaissance of research on the role of the spectral phase in single-channel speech enhancement. One of the recent proposals is to not only estimate the clean speech phase but also use this phase estimate as an additional source of information to facilitate the estimation of the clean speech magnitude. To assess the potential benefit of such approaches, in this paper we systematically explore in which situations additional information about the clean speech phase is most valuable. For this, we compare the performance of phase-aware and phase-blind clean speech estimators in different noise scenarios, i.e. at different signal to noise ratios (SNRs) and for noise sources with different degrees of stationarity. Interestingly, the results indicate that the greatest benefits can be achieved in situations where conventional magnitude-only speech enhancement is most challenging, namely in highly non-stationary noises at low SNRs.

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Authors and Affiliations

  1. Universität Hamburg, 20148, Hamburg, Germany

    Martin Krawczyk-Becker & Timo Gerkmann

Authors
  1. Martin Krawczyk-Becker
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  2. Timo Gerkmann
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Corresponding author

Correspondence to Martin Krawczyk-Becker .

Editor information

Editors and Affiliations

  1. Paul Sabatier University, Toulouse, France

    Yannick Deville

  2. Bar-Ilan University, Ramat Gan, Israel

    Sharon Gannot

  3. University of Surrey, Guildford, United Kingdom

    Russell Mason

  4. University of Surrey, Guildford, United Kingdom

    Mark D. Plumbley

  5. University of Surrey, Guildford, United Kingdom

    Dominic Ward

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Krawczyk-Becker, M., Gerkmann, T. (2018). A Study on the Benefits of Phase-Aware Speech Enhancement in Challenging Noise Scenarios. In: Deville, Y., Gannot, S., Mason, R., Plumbley, M., Ward, D. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2018. Lecture Notes in Computer Science(), vol 10891. Springer, Cham. https://doi.org/10.1007/978-3-319-93764-9_38

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  • DOI: https://doi.org/10.1007/978-3-319-93764-9_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93763-2

  • Online ISBN: 978-3-319-93764-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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