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

arXiv: https://arxiv.org/abs/2308.02418 · HTML: https://mlsanity.com/paper/2308.02418/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 17 references that are in the map.
- First posted on arXiv 26 Jul 2023.

## Builds on (most cited first, 2 of 2)

- [Optimizing Short-Time Fourier Transform Parameters via Gradient Descent](https://mlsanity.com/paper/2010.15049/) (2020)
- [A differentiable short-time Fourier transform with respect to the window length](https://mlsanity.com/paper/2208.10886/) (2022)

## Built on by (most cited first, 2 of 2)

- [Modulated differentiable STFT and balanced spectrum metric for freight train wheelset bearing cross-machine transfer monitoring under speed fluctuations](https://mlsanity.com/paper/2406.11917/) (2024)
- [Learnable Adaptive Time-Frequency Representation via Differentiable Short-Time Fourier Transform](https://mlsanity.com/paper/2506.21440/) (2025)

## Abstract (arXiv)

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.