Point process MCMC for sequential music transcription

In this paper, models and algorithms are presented for transcription of pitch and timings in polyphonic music extracts, focusing on the algorithm details of the sequential Markov chain Monte Carlo (MCMC) inference techniques used. The data are decomposed frame-wise into the frequency domain, where a Poisson point process model is used to write a polyphonic pitch likelihood function. A dynamical model is then used to link notes between frames. Inference in the model is carried out via Bayesian filtering using a sequential MCMC algorithm. The filtering procedure is sub-optimal, using some novel assumptions to render the task computationally tractable for large numbers of notes. Initial results with guitar music, both laboratory test data and commercial extracts, show promising performance.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Pete Bunch, Simon J. Godsill
• Keywords: ICASSP 2011, Markov Chain Monte Carlo, Polyphonic Pitch Likelihood, Sequential Mcmc Algorithm, Signal Processing