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dc.contributor.authorWANG, Cen_US
dc.contributor.authorBENETOS, Een_US
dc.contributor.authorMENG, Xen_US
dc.contributor.authorCHEW, Een_US
dc.contributor.authorSound and Music Computing Conferenceen_US
dc.date.accessioned2019-04-25T09:17:04Z
dc.date.available2019-03-22en_US
dc.date.issued2019-05-28en_US
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/57029
dc.description.abstractPlaying 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.en_US
dc.format.extent545 - 550en_US
dc.titleHMM-based Glissando Detection for Recordings of Chinese Bamboo Fluteen_US
dc.typeConference Proceeding
dc.rights.holder© 2019 The Author(s)
pubs.notesNot knownen_US
pubs.publication-statusAccepteden_US
dcterms.dateAccepted2019-03-22en_US
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US
qmul.funderA Machine Learning Framework for Audio Analysis and Retrieval::Royal Academy of Engineeringen_US


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