• DocumentCode
    61926
  • Title

    Compositional Models for Audio Processing: Uncovering the structure of sound mixtures

  • Author

    Virtanen, Tuomas ; Gemmeke, Jort Florent ; Raj, Bhiksha ; Smaragdis, Paris

  • Author_Institution
    Dept. of Signal Process., Tampere Univ. of Technol., Tampere, Finland
  • Volume
    32
  • Issue
    2
  • fYear
    2015
  • fDate
    Mar-15
  • Firstpage
    125
  • Lastpage
    144
  • Abstract
    Many classes of data are composed as constructive combinations of parts. By constructive combination, we mean additive combination that does not result in subtraction or diminishment of any of the parts. We will refer to such data as compositional data. Typical examples include population or counts data, where the total count of a population is obtained as the sum of counts of subpopulations. To characterize such data, various mathematical models have been developed in the literature. These models, in conformance with the nature of the data, represent them as nonnegative linear combinations of parts, which themselves are also nonnegative to ensure that such a combination does not result in subtraction or diminishment. We will refer to such models as compositional models.
  • Keywords
    audio signal processing; data handling; audio processing; compositional data; compositional models; counts data; mathematical models; nonnegative linear part combinations; population data; sound mixture structure; sum-of-subpopulation counts; Acoustic signal processing; Atomic clocks; Matrix decomposition; Principal component analysis; Signal resolution; Spectrogram; Time-frequency analysis;
  • fLanguage
    English
  • Journal_Title
    Signal Processing Magazine, IEEE
  • Publisher
    ieee
  • ISSN
    1053-5888
  • Type

    jour

  • DOI
    10.1109/MSP.2013.2288990
  • Filename
    7038275