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Adaptive Harmonic Spectral Decomposition for Multiple Pitch Estimation (2009)
| Citations: | 50 - 10 self |
BibTeX
@MISC{Vincent09adaptiveharmonic,
author = {Emmanuel Vincent and Nancy Bertin and Roland Badeau},
title = { Adaptive Harmonic Spectral Decomposition for Multiple Pitch Estimation},
year = {2009}
}
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Abstract
Multiple pitch estimation consists of estimating the fundamental frequencies and saliences of pitched sounds over short time frames of an audio signal. This task forms the basis of several applications in the particular context of musical audio. One approach is to decompose the short-term magnitude spectrum of the signal into a sum of basis spectra representing individual pitches scaled by time-varying amplitudes, using algorithms such as nonnegative matrix factorization (NMF). Prior training of the basis spectra is often infeasible due to the wide range of possible musical instruments. Appropriate spectra must then be adaptively estimated from the data, which may result in limited performance due to overfitting issues. In this article, we model each basis spectrum as a weighted sum of narrowband spectra representing a few adjacent harmonic partials, thus enforcing harmonicity and spectral smoothness while adapting the spectral envelope to each instrument. We derive a NMFlike algorithm to estimate the model parameters and evaluate it on a database of piano recordings, considering several choices for the narrowband spectra. The proposed algorithm performs similarly to supervised NMF using pre-trained piano spectra but improves pitch estimation performance by 6 % to 10 % compared to alternative unsupervised NMF algorithms.
Keyphrases
multiple pitch estimation basis spectrum adaptive harmonic spectral decomposition narrowband spectrum several application musical audio prior training piano recording short-term magnitude spectrum wide range audio signal nonnegative matrix factorization spectral smoothness unsupervised nmf algorithm possible musical instrument several choice particular context short time frame model parameter pitched sound time-varying amplitude appropriate spectrum pre-trained piano spectrum weighted sum adjacent harmonic partial limited performance pitch estimation performance nmflike algorithm fundamental frequency spectral envelope individual pitch
