Differentiable Adaptive Short-Time Fourier Transform with Respect to the Window Length
- Resource Type
- Conference
- Authors
- Leiber, Maxime; Marnissi, Yosra; Barrau, Axel; Badaoui, Mohammed El
- Source
- ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2023 - 2023 IEEE International Conference on. :1-5 Jun, 2023
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Vibrations
Time-frequency analysis
Fourier transforms
Signal processing
Acoustics
Transient analysis
Speech processing
Time-frequency
differentiable STFT
adaptive STFT
spectrogram
gradient descent
- Language
- ISSN
- 2379-190X
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.