Inferring Metrical Structure in Music Using Particle Filters
Sprache des Titels:
In this work, we propose a new state-of-the-art particle filter (PF) system to infer the metrical
structure of musical audio signals. The new inference method is designed to overcome the problem of
PFs in multi-modal probability distributions, which arise due to tempo and phase ambiguities in musical
rhythm representations. We compare the new method with a hidden Markov model (HMM) system and
several other PF schemes in terms of performance, speed and scalability on several audio datasets. We
demonstrate that using the proposed system the computational complexity can be reduced drastically in
comparison to the HMM while maintaining the same order of beat tracking accuracy. Therefore, for the
first time, the proposed system allows fast meter inference in a high-dimensional state space, spanned
by the three components of tempo, type of rhythm, and position in a metric cycle.
Sprache der Kurzfassung:
IEEE/ACM Transactions on Audio, Speech, and Language Processing 2015.