Efficient Learning of Harmonic Priors for Pitch Detection in Polyphonic Music
Pablo A. Alvarado, Dan Stowell

TL;DR
This paper explores the use of physically inspired Gaussian process priors, specifically the Matérn spectral mixture kernel, to improve polyphonic music transcription by better modeling harmonic content and pitch activations.
Contribution
It introduces a novel MSM kernel for frequency modeling and compares sigmoid and softmax regression approaches for pitch detection in polyphonic music.
Findings
MSM kernel effectively models note frequency content.
Learning priors that fit sound event frequencies improves pitch detection.
Cross-correlation between activations is less beneficial than frequency-aligned priors.
Abstract
Automatic music transcription (AMT) aims to infer a latent symbolic representation of a piece of music (piano-roll), given a corresponding observed audio recording. Transcribing polyphonic music (when multiple notes are played simultaneously) is a challenging problem, due to highly structured overlapping between harmonics. We study whether the introduction of physically inspired Gaussian process (GP) priors into audio content analysis models improves the extraction of patterns required for AMT. Audio signals are described as a linear combination of sources. Each source is decomposed into the product of an amplitude-envelope, and a quasi-periodic component process. We introduce the Mat\'ern spectral mixture (MSM) kernel for describing frequency content of singles notes. We consider two different regression approaches. In the sigmoid model every pitch-activation is independently…
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Taxonomy
TopicsMusic and Audio Processing · Gaussian Processes and Bayesian Inference · Time Series Analysis and Forecasting
