Separation of harmonic sounds using linear models for the overtone series

Abstract

A signal processing method is described, which separates harmonic sounds by applying linear models for the overtone series of sounds. Time-varying sinusoidal parameters are estimated in an iterative algorithm which is initialized using a multipitch estimator that finds the number of concurrent sounds and their frequency components. The iterative process then improves the estimates using the least-squares criterion. The harmonic stucture is retained by keeping the frequency ratio of overtones constant over time. Overlapping frequency components are resolved by using linear models for the overtone amplitudes. In practice, the models retain the spectral continuity of natural sounds. Simulation experiments were done using some basic structures for the linear models. These include polynomial, mel-cepstal and frequency-band model. Demonstration signals are available at

Musical Instrument Timbres Classification with Spectral Features

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Harmonic and inharmonic Nonnegative Matrix Factorization for Polyphonic Pitch transcription

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Query-by-Example Music Information Retrieval by Score-Informed Source Separation and Remixing Technologies

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“…Although a number of sound source separation methods [11][12][13][14] have been studied, most of them still focus on dealing with music performed on either pitched instruments that have harmonic sounds or drums that have inharmonic sounds. For example, most separation methods for harmonic sounds [11][12][13][14] cannot separate inharmonic sounds, while most separation methods for inharmonic sounds, such as drums [15], cannot separate harmonic ones. Sound source separation methods based on the stochastic properties of audio signals, for example, independent component analysis and sparse coding [16][17][18], treat particular kind of audio signals which are recorded with a microphone array or have small number of simultaneously voiced musical notes.…”