Playing Technique Recognition by Joint Time-Frequency Scattering

Abstract

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.