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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/128611


    Title: A speech enhancement method using Fast Fourier Transform and Convolutional Autoencoder
    Authors: Kow, Pu-Yun;Kow, Pu-Zhao
    Keywords: Inverse problem;Fourier transform;Convolutional-based Autoencoder (ConvAE);convolutional neural network (CNN);artificial neural network (ANN);Artificial Intelligent (AI)
    Date: 2025-11-10
    Issue Date: 2026-03-06 12:08:07 (UTC+8)
    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 simple methods for effective speech reconstruction.
    Relation: Applied Mathematics for Modern Challenges 6, p.1-14
    DOI: 10.48550/arXiv.2501.01650
    Appears in Collections:[Department of Artificial Intelligence] Journal Article

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