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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. ©2007 Acoustical Society of America

Contents

• Introduction
• Poisson point processes
• Rate function estimation
• Maximum-likelihood transcription
• Results
• Conclusion and discussion
• REFERENCES
• FIGURES
• TABLES