Towards HMM-based glissando detection for recordings of Chinese bamboo flute

Playing techniques such as ornamentations and articulation effects constitute important aspects of music performance. However, their computational analysis is still under-explored due to a lack of data and established methods. Focusing on the Chinese bamboo flute, we introduce a two-stage glissando detection system based on hidden Markov models (HMMs) with Gaussian mixtures. A rule-based segmentation process extracts glissando candidates that are consecutive note changes in the same direction. Glissandi are then identified by two HMMs (glissando and non-glissando). The study uses a newly created dataset of Chinese bamboo flute recordings. The results, based on both frame- and segment-based evaluation, achieve F-measures of 78% and 73% for ascending glissandi, and 65% and 72% for descending glissandi, respectively. The dataset and method can be used for performance analysis