Adaptive Harmonic Time–Frequency Decomposition of Audio using Shift-Invariant PLCA

Numerous methods have been developed for the time-frequency analysis and smart decomposition of audio signals. However, these techniques are not consistently suitable for real music signals where each note presents continuous variations of both pitch and spectral envelope. This paper presents a new model for analyzing the har-monic structures of an audio signal that can jointly handle those two types of variations. Each note in a constant-Q transform is modeled as a weighted sum of narrowband parametric spectra, and positive deconvolution is performed to estimate the model parameters, in the framework of probabilistic latent component analysis. The algo-rithm has been tested in a task of monopitch estimation. The very promising results highlight the reliability and the robustness of the model. Index Terms — Probabilistic latent component analysis, har-monicity, nonnegative matrix factorization, pitch estimation. 1