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4 mai 2020
- ISIDORE Id: 10670/1.36b317...
- hal: hal-03277686
- doi: 10.1109/ICASSP40776.2020.9053474
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info:eu-repo/semantics/altIdentifier/doi/10.1109/ICASSP40776.2020.9053474
info:eu-repo/semantics/OpenAccess
Mots-clés
Music signal analysis scattering transform performance analysis playing technique recognitionSujets proches
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Changhong Wang et al., « Playing Technique Recognition by Joint Time–Frequency Scattering », HAL SHS (Sciences de l’Homme et de la Société), ID : 10.1109/ICASSP40776.2020.9053474
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Résumé
Playing techniques are important expressive elements in music signals. In this paper, we propose a recognition system based on the joint time-frequency scattering transform (jTFST) for pitch evolution-based playing techniques (PETs), a group of playing techniques with monotonic pitch changes over time. The jTFST represents spectro-temporal patterns in the time-frequency domain, capturing discriminative information of PETs. As a case study, we analyse three commonly used PETs of the Chinese bamboo flute: acciacatura, portamento, and glissando, and encode their characteristics using the jTFST. To verify the proposed approach, we create a new dataset, the CBF-petsDB, containing PETs played in isolation as well as in the context of whole pieces performed and annotated by professional players. Feeding the jTFST to a machine learning classifier, we obtain F-measures of 71% for acciacatura, 59% for portamento, and 83% for glissando detection, and provide explanatory visualisations of scattering coefficients for each technique.
