Audio part mixture alignment based on hierarchical nonparametric Bayesian model of musical audio sequence collection
Akira Maezawa, Hiroshi G. Okuno · 2014
This paper proposes “audio part mixture alignment,” a method for temporally aligning multiple audio signals, each of which is a rendition of a non-disjoint subset of a common piece of music. The method decomposes each audio signal into shared components and components unique to each rendition. At the same time, it aligns each audio signal based on the shared component. Decomposition of audio signal is modeled using a hierarchical Dirichlet process (Hierarchical DP, HDP), and sequence alignment is modeled as a left-to-right hidden Markov model (HMM). Variational Bayesian inference is used to jointly infer the alignment and component decomposition. The proposed method is compared with a classic audio-to-audio alignment method, and it is found that the proposed method is more robust to the discrepancy of parts between two audio signals.