| A speech enhancement method using Fast Fourier Transform and Convolutional Autoencoder | |
|---|---|
| 學年 | 114 |
| 學期 | 1 |
| 出版（發表）日期 | 2025-11-10 |
| 作品名稱 | A speech enhancement method using Fast Fourier Transform and Convolutional Autoencoder |
| 作品名稱（其他語言） | |
| 著者 | Pu-Yun Kow; Pu-Zhao Kow |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Applied Mathematics for Modern Challenges 6, p.1-14 |
| 摘要 | 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 simple methods for effective speech reconstruction. |
| 關鍵字 | Inverse problem; Fourier transform; Convolutional-based Autoencoder (ConvAE); convolutional neural network (CNN); artificial neural network (ANN); Artificial Intelligent (AI) |
| 語言 | en |
| ISSN | 2994-7669 |
| 期刊性質 | 國外 |
| 收錄於 | |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 是 |
| 國別 | GBR |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/128611 ) |
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