A Speech Enhancement Method Using Fast Fourier Transform and Convolutional Autoencoder

2025/01/03 by Kow, Pu-Yun, Kow, Pu-Zhao
#35R25 #35R30 #68T07 #68T10 #68T20 #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Sound (cs.SD) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2501.01650

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.

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• FFC-SE: Fast Fourier Convolution for Speech Enhancement
• Raw Waveform-based Speech Enhancement by Fully Convolutional Networks
• Speech Dereverberation Using Fully Convolutional Networks
• Helsinki Speech Challenge 2024
• A Perceptually-Motivated Approach for Low-Complexity, Real-Time Enhancement of Fullband Speech