• Login
    JavaScript is disabled for your browser. Some features of this site may not work without it.
    PIANO SUSTAIN-PEDAL DETECTION USING CONVOLUTIONAL NEURAL NETWORKS 
    •   QMRO Home
    • School of Electronic Engineering and Computer Science
    • Electronic Engineering and Computer Science
    • PIANO SUSTAIN-PEDAL DETECTION USING CONVOLUTIONAL NEURAL NETWORKS
    •   QMRO Home
    • School of Electronic Engineering and Computer Science
    • Electronic Engineering and Computer Science
    • PIANO SUSTAIN-PEDAL DETECTION USING CONVOLUTIONAL NEURAL NETWORKS
    ‌
    ‌

    Browse

    All of QMROCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects
    ‌
    ‌

    Administrators only

    Login
    ‌
    ‌

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    PIANO SUSTAIN-PEDAL DETECTION USING CONVOLUTIONAL NEURAL NETWORKS

    View/Open
    Accepted.pdf (501.7Kb)
    Pagination
    241 - 245
    ISSN
    1520-6149
    Metadata
    Show full item record
    Authors
    Liang, B; Fazekas, G; Sandler, M; IEEE
    URI
    https://qmro.qmul.ac.uk/xmlui/handle/123456789/68122
    Collections
    • Electronic Engineering and Computer Science [2826]
    Copyright statements
    © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    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.