Polyphony multi-pitch estimation method and application of pseudo bispectra to multi-pitch estimation

Abstract

The invention belongs to the field of digital speech signal processing, and discloses a polyphony multi-pitch estimation method and application of pseudo bispectra to multi-pitch estimation to solve the problem of multi-pitch estimation of polyphony. The method is technically characterized by comprising the steps of framing input audio frequency; solving the pseudo bispectrum of each frame of signals, ranking the values of the matching cross-correlation function between a two-dimensional template and pseudo bispectra from large to small, and taking the first several frequencies as candidate pitches; calculating the weighting harmonic energy sum of each candidate pitch, and selecting the candidate pitch with the largest weighting harmonic energy sum as the most significantly estimated pitch output by this time of iteration. The method has the advantages that normal work can be conducted when the number of harmonic components is small, notes with overlapped harmonic frequency components can be distinguished, calculation amount is small, and the method is easy to realize and can also be used for base frequency extraction of harmonic signals besides polyphony.

Description

Multi-pitch Estimation Method for Polyphonic Music and Application of Pseudo-Bispectrum in Multi-pitch Estimation technical field The invention belongs to the field of digital voice signal processing and relates to a music signal processing method. Background technique Based on the algorithm principle, polyphonic music multi-pitch estimation methods can be divided into feature-based, statistical model-based and spectral decomposition-based methods, most of which are based on one-dimensional Fourier transform spectrum. When different notes have the same harmonic frequency components, the one-dimensional Fourier transform spectrum cannot separate these overlapping harmonic frequency components. Harmony is one of the basic elements of music, so it is common to have overlapping harmonic frequency components in music signals, so it is of great significance to accurately separate notes with overlapping harmonic frequencies. Recently, Argenti et al. proposed a bispectrum-based ...

AI Technical Summary

Problems solved by technology

However, the bispectral amplitude of the signal is the product of the amplitudes of the three frequency components of the one-dimensional Fourier transform spectrum, so any one of the components being 0 will result in a bispectral amplitude of 0, which in turn makes the two-dimensional template matching fail

In addition, due to spectral leakage, the multi-pitch estimation method based on bispectrum will produce more low-octave errors

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