A Speech Enhancement Method Using Fast Fourier Transform and Convolutional Autoencoder
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866914154811490304 |
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| author | Kow, Pu-Yun Kow, Pu-Zhao |
| author_facet | Kow, Pu-Yun Kow, Pu-Zhao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_01650 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Speech Enhancement Method Using Fast Fourier Transform and Convolutional Autoencoder Kow, Pu-Yun Kow, Pu-Zhao Sound Audio and Speech Processing 68T07, 68T10, 68T20, 35R25, 35R30 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. |
| title | A Speech Enhancement Method Using Fast Fourier Transform and Convolutional Autoencoder |
| topic | Sound Audio and Speech Processing 68T07, 68T10, 68T20, 35R25, 35R30 |
| url | https://arxiv.org/abs/2501.01650 |