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Differentiable adaptive short-time Fourier transform with respect to the window length

Authors :
Leiber, Maxime
Marnissi, Yosra
Barrau, Axel
Badaoui, Mohammed El
Publication Year :
2023

Abstract

This paper presents a gradient-based method for on-the-fly optimization for both per-frame and per-frequency window length of the short-time Fourier transform (STFT), related to previous work in which we developed a differentiable version of STFT by making the window length a continuous parameter. The resulting differentiable adaptive STFT possesses commendable properties, such as the ability to adapt in the same time-frequency representation to both transient and stationary components, while being easily optimized by gradient descent. We validate the performance of our method in vibration analysis.<br />Comment: Accepted for IEEE International Conference on Acoustics, Speech, and SIgnal Processing (ICASSP) 2023

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2308.02418
Document Type :
Working Paper
Full Text :
https://doi.org/10.1109/ICASSP49357.2023.10095245