# Spectral analysis for nonstationary audio

arXiv: https://arxiv.org/abs/1712.10252 · HTML: https://mlsanity.com/paper/1712.10252/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2017.

## Built on by (most cited first, 1 of 1)

- [Time-warping estimation via stationarity-based learning of the de-warped signal](https://mlsanity.com/paper/2609.16796/) (2026)

## Abstract (arXiv)

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.