Adaptive Scattering Transforms for Playing Technique Recognition,IEEE/ACM Transactions on Audio, Speech, and Language Processing

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Adaptive Scattering Transforms for Playing Technique Recognition
IEEE/ACM Transactions on Audio, Speech, and Language Processing ( IF 4.1 ) Pub Date : 2022-03-07 , DOI: 10.1109/taslp.2022.3156785
Changhong Wang ₁ , Emmanouil Benetos ₁ , Vincent Lostanlen ₂ , Elaine Chew ₃

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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.

更新日期：2022-03-07

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