Adaptive Scattering Transforms for Playing Technique Recognition

Changhong Wang*, Emmanouil Benetos, Vincent Lostanlen, Elaine Chew

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
72 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)1407-1421
Number of pages15
JournalIEEE/ACM Transactions on Audio, Speech, and Language Processing
Publication statusPublished - 7 Mar 2022


  • Scattering
  • Wavelet transforms
  • Instruments
  • Time-frequency analysis
  • Multiple signal classification
  • Music
  • Speech processing
  • Music performance analysis
  • music signal analysis
  • scattering transform


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