| dc.contributor | 應數系 | |
| dc.creator (作者) | 邱普照 | |
| dc.creator (作者) | Kow, Pu-Zhao;Kow, Pu-Yun | |
| dc.date (日期) | 2025-12 | |
| dc.date.accessioned | 9-Jan-2026 10:09:21 (UTC+8) | - |
| dc.date.available | 9-Jan-2026 10:09:21 (UTC+8) | - |
| dc.date.issued (上傳時間) | 9-Jan-2026 10:09:21 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/161019 | - |
| dc.description.abstract (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. | |
| dc.format.extent | 100 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Applied Mathematics for Modern Challenges, Vol.6, pp.1-14 | |
| dc.subject (關鍵詞) | Inverse problem; Fourier transform; Convolutional-based Autoencoder (ConvAE); convolutional neural network (CNN); artificial neural network (ANN); Artificial Intelligent (AI) | |
| dc.title (題名) | A speech enhancement method using Fast Fourier Transform and Convolutional Autoencoder | |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.3934/ammc.2025013 | |
| dc.doi.uri (DOI) | https://doi.org/10.3934/ammc.2025013 | |
