Spectral analysis for non-stationary audio

12/29/2017
by   Adrien Meynard, et al.
0

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

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/29/2017

Spectral analysis for nonstationary audio

A new approach for the analysis of nonstationary signals is proposed, wi...
research
01/27/2018

Parametric Modeling of Non-Stationary Signals

Parametric modeling of non-stationary signals is addressed in this artic...
research
05/06/2022

Trainable Wavelet Neural Network for Non-Stationary Signals

This work introduces a wavelet neural network to learn a filter-bank spe...
research
09/09/2023

RRCNN^+: An Enhanced Residual Recursive Convolutional Neural Network for Non-stationary Signal Decomposition

Time-frequency analysis is an important and challenging task in many app...
research
06/29/2020

Parametric Modeling of EEG by Mono-Component Non-Stationary Signal

In this paper, we propose a novel approach for parametric modeling of el...
research
06/16/2018

Wavelet regression: An approach for undertaking multi-time scale analyses of hydro-climate relationships

Previous studies showed that hydro-climate processes are stochastic and ...
research
08/05/2022

AID: Open-source Anechoic Interferer Dataset

A dataset of anechoic recordings of various sound sources encountered in...

Please sign up or login with your details

Forgot password? Click here to reset