Spectral analysis for nonstationary audio

TL;DR

This paper introduces JEFAS, a novel spectral analysis method for nonstationary audio signals, using wavelet-based maximum likelihood estimation to handle time warping and amplitude modulation.

Contribution

It presents a new wavelet-based maximum likelihood approach for analyzing nonstationary audio signals, with theoretical validation and a practical algorithm called JEFAS.

Findings

Effective on synthetic and real audio signals

Accurate estimation of time warping and amplitude modulation

Theoretical analysis supports approximation validity

Abstract

A new approach for the analysis of nonstationary signals is proposed, with a focus on audio applications. Following earlier contributions, nonstationarity is modeled via stationarity-breaking operators acting on Gaussian stationary random signals. The focus is on time warping and amplitude modulation, and an approximate maximum-likelihood approach based on suitable approximations in the wavelet transform domain is developed. This paper provides theoretical analysis of the approximations, and introduces JEFAS, a corresponding estimation algorithm. The latter is tested and validated on synthetic as well as real audio signal.