A Speech Enhancement Method Using Fast Fourier Transform and Convolutional Autoencoder
Sound
2025-11-14 v2 Audio and Speech Processing
Abstract
This paper addresses the reconstruction of audio signals from degraded measurements. We propose a lightweight model that combines the discrete Fourier transform with a Convolutional Autoencoder (FFT-ConvAE), which enabled our team to achieve second place in the Helsinki Speech Challenge 2024. Our results, together with those of other teams, demonstrate the potential of neural-network-free approaches for effective speech signal reconstruction.
Cite
@article{arxiv.2501.01650,
title = {A Speech Enhancement Method Using Fast Fourier Transform and Convolutional Autoencoder},
author = {Pu-Yun Kow and Pu-Zhao Kow},
journal= {arXiv preprint arXiv:2501.01650},
year = {2025}
}
Comments
The paper has been reorganized, and its title has been revised