Spectral analysis for nonstationary audio
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.
Cite
@article{arxiv.1712.10252,
title = {Spectral analysis for nonstationary audio},
author = {Adrien Meynard and Bruno Torrésani},
journal= {arXiv preprint arXiv:1712.10252},
year = {2018}
}
Comments
IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, In press