• Login
    JavaScript is disabled for your browser. Some features of this site may not work without it.
    Audio Source Separation Using Sparse Representations 
    •   QMRO Home
    • School of Electronic Engineering and Computer Science
    • Electronic Engineering and Computer Science
    • Audio Source Separation Using Sparse Representations
    •   QMRO Home
    • School of Electronic Engineering and Computer Science
    • Electronic Engineering and Computer Science
    • Audio Source Separation Using Sparse Representations
    ‌
    ‌

    Browse

    All of QMROCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects
    ‌
    ‌

    Administrators only

    Login
    ‌
    ‌

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Audio Source Separation Using Sparse Representations

    View/Open
    Accepted version (345.5Kb)
    Editors
    Wang, W
    Pagination
    246 - 264 (18)
    Publisher
    IGI Global
    Publisher URL
    http://www.igi-global.com/chapter/audio-source-separation-using-sparse/45488
    ISBN-10
    1615209190
    ISBN-13
    9781615209194
    DOI
    10.4018/978-1-61520-919-4.ch010
    Journal
    Machine Audition: Principles, Algorithms and Systems
    Metadata
    Show full item record
    Abstract
    The authors address the problem of audio source separation, namely, the recovery of audio signals from recordings of mixtures of those signals. The sparse component analysis framework is a powerful method for achieving this. Sparse orthogonal transforms, in which only few transform coefficients differ significantly from zero, are developed; once the signal has been transformed, energy is apportioned from each transform coefficient to each estimated source, and, finally, the signal is reconstructed using the inverse transform. The overriding aim of this chapter is to demonstrate how this framework, as exemplified here by two different decomposition methods which adapt to the signal to represent it sparsely, can be used to solve different problems in different mixing scenarios. To address the instantaneous (neither delays nor echoes) and underdetermined (more sources than mixtures) mixing model, a lapped orthogonal transform is adapted to the signal by selecting a basis from a library of predetermined bases. This method is highly related to the windowing methods used in the MPEG audio coding framework. In considering the anechoic (delays but no echoes) and determined (equal number of sources and mixtures) mixing case, a greedy adaptive transform is used based on orthogonal basis functions that are learned from the observed data, instead of being selected from a predetermined library of bases. This is found to encode the signal characteristics, by introducing a feedback system between the bases and the observed data. Experiments on mixtures of speech and music signals demonstrate that these methods give good signal approximations and separation performance, and indicate promising directions for future research.
    Authors
    Nesbit, A; Jafari, MG; Vincent, E; Plumbley, MD
    URI
    http://qmro.qmul.ac.uk/xmlui/handle/123456789/5268
    Collections
    • Electronic Engineering and Computer Science [2816]
    Twitter iconFollow QMUL on Twitter
    Twitter iconFollow QM Research
    Online on twitter
    Facebook iconLike us on Facebook
    • Site Map
    • Privacy and cookies
    • Disclaimer
    • Accessibility
    • Contacts
    • Intranet
    • Current students

    Modern Slavery Statement

    Queen Mary University of London
    Mile End Road
    London E1 4NS
    Tel: +44 (0)20 7882 5555

    © Queen Mary University of London.