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Singing Voice Separation Using RPCA with Weighted \(l_{1}\)-norm

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Latent Variable Analysis and Signal Separation (LVA/ICA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10169))

Abstract

In this paper, we present an extension of robust principal component analysis (RPCA) with weighted \(l_{1}\)-norm minimization for singing voice separation. While the conventional RPCA applies a uniform weight between the low-rank and sparse matrices, we use different weighting parameters for each frequency bin in a spectrogram by estimating the variance ratio between the singing voice and accompaniment. In addition, we incorporate the results of vocal activation detection into the formation of the weighting matrix, and use it in the final decomposition framework. From the experimental results using the DSD100 dataset, we found that proposed algorithm yields a meaningful improvement in the separation performance compared to the conventional RPCA.

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Acknowledgments

This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2016-H8501-16-1016) supervised by the IITP (Institute for Information & communications Technology Promotion)

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Authors and Affiliations

  1. Music and Audio Research Group, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Korea

    Il-Young Jeong & Kyogu Lee

Authors
  1. Il-Young Jeong
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  2. Kyogu Lee
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Corresponding author

Correspondence to Kyogu Lee .

Editor information

Editors and Affiliations

  1. Institute of Information Theory and Automation, Prague, Czech Republic

    Petr Tichavský

  2. Sharif University of Technology, Tehran, Iran

    Massoud Babaie-Zadeh

  3. Grenoble-Alpes University, Grenoble, France

    Olivier J.J. Michel

  4. Toulon University, Toulon, France

    Nadège Thirion-Moreau

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Jeong, IY., Lee, K. (2017). Singing Voice Separation Using RPCA with Weighted \(l_{1}\)-norm. In: Tichavský, P., Babaie-Zadeh, M., Michel, O., Thirion-Moreau, N. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2017. Lecture Notes in Computer Science(), vol 10169. Springer, Cham. https://doi.org/10.1007/978-3-319-53547-0_52

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  • DOI: https://doi.org/10.1007/978-3-319-53547-0_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53546-3

  • Online ISBN: 978-3-319-53547-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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