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Journal Articles IEEE/ACM Transactions on Audio, Speech and Language Processing Year : 2022

Adaptive Scattering Transforms for Playing Technique Recognition

Abstract

Playing techniques contain distinctive information about musical expressivity and interpretation. Yet, current research in music signal analysis suffers from a scarcity of computational models for playing techniques, especially in the context of live performance. To address this problem, our paper develops a general framework for playing technique recognition. We propose the adaptive scattering transform, which refers to any scattering transform that includes a stage of data-driven dimensionality reduction over at least one of its wavelet variables, for representing playing techniques. Two adaptive scattering features are presented: frequency-adaptive scattering and direction-adaptive scattering. We analyse seven playing techniques: vibrato, tremolo, trill, flutter-tongue, acciaccatura, portamento, and glissando. To evaluate the proposed methodology, we create a new dataset containing full-length Chinese bamboo flute performances (CBFdataset) with expert playing technique annotations. Once trained on the proposed scattering representations, a support vector classifier achieves state-of-the-art results. We provide explanatory visualisations of scattering coefficients for each technique and verify the system over three additional datasets with various instrumental and vocal techniques: VPset, SOL, and VocalSet.
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Dates and versions

hal-03629482 , version 1 (04-04-2022)
hal-03629482 , version 2 (06-08-2022)

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Changhong Wang, Emmanouil Benetos, Vincent Lostanlen, Elaine Chew. Adaptive Scattering Transforms for Playing Technique Recognition. IEEE/ACM Transactions on Audio, Speech and Language Processing, 2022, 30, pp.1407-1421. ⟨10.1109/TASLP.2022.3156785⟩. ⟨hal-03629482v2⟩
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