A Speech Enhancement Method Using Fast Fourier Transform and Convolutional Autoencoder
Pu-Yun Kow, Pu-Zhao Kow
Published: 2025/1/3
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.