Differentiable Adaptive Short-Time Fourier Transform with Respect to the Window Length

Maxime Leiber; Yosra Marnissi; Axel Barrau; Mohamed El Badaoui

ICASSP 2023accepted0 citations

Differentiable Adaptive Short-Time Fourier Transform with Respect to the Window Length

Maxime Leiber, Yosra Marnissi, Axel Barrau, Mohamed El Badaoui

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.

BibTeX

@inproceedings{icassp2023_differentiablead,
  title = {Differentiable Adaptive Short-Time Fourier Transform with Respect to the Window Length},
  author = {Maxime Leiber and Yosra Marnissi and Axel Barrau and Mohamed El Badaoui},
  booktitle = {ICASSP 2023},
  year = {2023}
}