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Nonnegative Matrix Factorization with Markov-Chained Bases for Modeling Time-Varying Patterns in Music Spectrograms

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Latent Variable Analysis and Signal Separation (LVA/ICA 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6365))

Abstract

This paper presents a new sparse representation for polyphonic music signals. The goal is to learn the time-varying spectral patterns of musical instruments, such as attack of the piano or vibrato of the violin in polyphonic music signals without any prior information. We model the spectrogram of music signals under the assumption that they are composed of a limited number of components which are composed of Markov-chained spectral patterns. The proposed model is an extension of nonnegative matrix factorization (NMF). An efficient algorithm is derived based on the auxiliary function method.

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References

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Author information

Authors and Affiliations

  1. Graduate School of Information Science and Technology, The University of Tokyo,  

    Masahiro Nakano, Yu Kitano, Nobutaka Ono & Shigeki Sagayama

  2. NTT Communication Science Laboratories, NTT Corporation,  

    Jonathan Le Roux & Hirokazu Kameoka

Authors
  1. Masahiro Nakano
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  2. Jonathan Le Roux
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  3. Hirokazu Kameoka
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  4. Yu Kitano
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  5. Nobutaka Ono
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  6. Shigeki Sagayama
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Editor information

Editors and Affiliations

  1. Dept. of Electrical Engineering, Universitè d’Evry Val d’Essone, 40 rue du Pelvoux, 91020, Courcouronnes, France

    Vincent Vigneron

  2. Laboratoire I3S, Les Algorithmes - Euclide-B, BP 121, Université de Nice-Sophia Antipolis, 2000 Route des Lucioles, 06903, Sophia Antipolis Cedex, France

    Vicente Zarzoso

  3. School of Engineering, Dept. of Telecommunications, ISITSchool of Engineering, Dept. of Telecommunications, ISITV, Université de Toulon, Avenue George Pompidou, BP 56, La Valette du Var, Cedex, 83162, France

    Eric Moreau

  4. INRIA France, Equipe-projet METISS, Centre de Recherche INRIA Rennes-Bretagne Atlantique, Campus de Beaulieu, 35042, Rennes cedex, France

    Rémi Gribonval

  5. INRIA France, Equipe-projet METISS, Centre de Recherche INRIA Rennes-Bretagne Atlantique, Campus de Beaulieu, 35042, Rennes Cedex, France

    Emmanuel Vincent

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Nakano, M., Le Roux, J., Kameoka, H., Kitano, Y., Ono, N., Sagayama, S. (2010). Nonnegative Matrix Factorization with Markov-Chained Bases for Modeling Time-Varying Patterns in Music Spectrograms. In: Vigneron, V., Zarzoso, V., Moreau, E., Gribonval, R., Vincent, E. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2010. Lecture Notes in Computer Science, vol 6365. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15995-4_19

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  • DOI: https://doi.org/10.1007/978-3-642-15995-4_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15994-7

  • Online ISBN: 978-3-642-15995-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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