HMM-based Glissando Detection for Recordings of Chinese Bamboo Flute

Abstract

Playing techniques such as ornamentations and articulation effects constitute important aspects of music performance. However, their computational analysis is still at an early stage due to a lack of instrument diversity, established methodologies and informative data. 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. The study uses a newly created dataset of Chinese bamboo flute recordings, including both isolated glissandi and real-world pieces. The results, based on both frame- and segment-based evaluation for ascending and descending glissandi respectively, confirm the feasibility of the proposed method for glissando detection. Better detection performance of ascending glissandi over descending ones is obtained due to their more regular patterns. Inaccurate pitch estimation forms a main obstacle for successful fully-automated glissando detection. The dataset and method can be used for performance analysis.