title: Poisson point process modeling for polyphonic music transcription.
abstract: 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.
authors.affiliation: Signal Processing Group, Department of Engineering, University of Cambridge, United Kingdom. php23@eng.cam.ac.uk
authors.forename: Paul
authors.initials: P
authors.lastname: Peeling
authors.forename: Chung-fai
authors.initials: CF
authors.lastname: Li
authors.forename: Simon
authors.initials: S
authors.lastname: Godsill
mesh_terms: D006801:Humans; D008962:Models, Theoretical; D009146:Music; D010898:Pitch Perception; D016012:Poisson Distribution