每年專案
摘要
Recently, model compression that aims to facilitate the use of deep models in real-world applications has attracted considerable attention. Several model compression techniques have been proposed to reduce computational costs without significantly degrading the achievable performance. In this paper, we propose a multimodal framework for speech enhancement (SE) by utilizing a hierarchical extreme learning machine (HELM) to enhance the performance of conventional HELM-based SE frameworks that consider audio information only. Furthermore, we investigate the performance of the HELM-based multimodal SE framework trained using binary weights and quantized input data to reduce the computational requirement. The experimental results show that the proposed multimodal SE framework outperforms the conventional HELM-based SE framework in terms of three standard objective evaluation metrics. The results also show that the performance of the proposed multimodal SE framework is only slightly degraded, when the model is compressed through model binarization and quantized input data.
原文 | ???core.languages.en_GB??? |
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主出版物標題 | 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019 |
發行者 | Institute of Electrical and Electronics Engineers Inc. |
頁面 | 678-683 |
頁數 | 6 |
ISBN(電子) | 9781728132488 |
DOIs | |
出版狀態 | 已出版 - 11月 2019 |
事件 | 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019 - Lanzhou, China 持續時間: 18 11月 2019 → 21 11月 2019 |
出版系列
名字 | 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019 |
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???event.eventtypes.event.conference??? | 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019 |
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國家/地區 | China |
城市 | Lanzhou |
期間 | 18/11/19 → 21/11/19 |
指紋
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