Poisson point process modeling for polyphonic music transcription.
Peaks detected in the frequency domain spectrum of a musical chord are modeled as realizations of a nonhomogeneous Poisson point process. When several notes are superimposed to make a chord, the processes for individual notes combine to give another Poisson process, whose likelihood is easily computable. This avoids a data association step linking individual harmonics explicitly with detected peaks in the spectrum. The likelihood function is ideal for Bayesian inference about the unknown note frequencies in a chord. Here, maximum likelihood estimation of fundamental frequencies shows very promising performance on real polyphonic piano music recordings.
Peeling
Paul
P
Signal Processing Group, Department of Engineering, University of Cambridge, United Kingdom. php23@eng.cam.ac.uk
Li
Chung-fai
CF
Godsill
Simon
S
D006801:Humans; D008962:Models, Theoretical; D009146:Music; D010898:Pitch Perception; D016012:Poisson Distribution