• Login
    JavaScript is disabled for your browser. Some features of this site may not work without it.
    HMM-based Glissando Detection for Recordings of Chinese Bamboo Flute 
    •   QMRO Home
    • School of Electronic Engineering and Computer Science
    • Electronic Engineering and Computer Science
    • HMM-based Glissando Detection for Recordings of Chinese Bamboo Flute
    •   QMRO Home
    • School of Electronic Engineering and Computer Science
    • Electronic Engineering and Computer Science
    • HMM-based Glissando Detection for Recordings of Chinese Bamboo Flute
    ‌
    ‌

    Browse

    All of QMROCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects
    ‌
    ‌

    Administrators only

    Login
    ‌
    ‌

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    HMM-based Glissando Detection for Recordings of Chinese Bamboo Flute

    View/Open
    Published version (577.9Kb)
    Pagination
    545 - 550
    Metadata
    Show full item record
    Abstract
    Playing techniques such as ornamentations and articulation effects constitute important aspects of music performance. However, their computational analysis is still at an early stage due to a lack of instrument diversity, established methodologies and informative data. Focusing on the Chinese bamboo flute, we introduce a two-stage glissando detection system based on hidden Markov models (HMMs) with Gaussian mixtures. A rule-based segmentation process extracts glissando candidates that are consecutive note changes in the same direction. Glissandi are then identified by two HMMs. The study uses a newly created dataset of Chinese bamboo flute recordings, including both isolated glissandi and real-world pieces. The results, based on both frame- and segment-based evaluation for ascending and descending glissandi respectively, confirm the feasibility of the proposed method for glissando detection. Better detection performance of ascending glissandi over descending ones is obtained due to their more regular patterns. Inaccurate pitch estimation forms a main obstacle for successful fully-automated glissando detection. The dataset and method can be used for performance analysis.
    Authors
    WANG, C; BENETOS, E; MENG, X; CHEW, E; Sound and Music Computing Conference
    URI
    https://qmro.qmul.ac.uk/xmlui/handle/123456789/57029
    Collections
    • Electronic Engineering and Computer Science [2674]
    Copyright statements
    © 2019 The Author(s)
    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.