Skip to main content

Playing Technique Recognition By Joint Time–Frequency Scattering

Changhong Wang, Emmanouil Benetos, Vincent Lostanlen, Elaine Chew

  • SPS
    Members: Free
    IEEE Members: $11.00
    Non-members: $15.00
    Length: 14:13
04 May 2020

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.

Value-Added Bundle(s) Including this Product

More Like This

  • SPS
    Members: $150.00
    IEEE Members: $250.00
    Non-members: $350.00
  • SPS
    Members: $150.00
    IEEE Members: $250.00
    Non-members: $350.00
  • SPS
    Members: $150.00
    IEEE Members: $250.00
    Non-members: $350.00