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pro vyhledávání: '"Kao, Chieh"'
Acoustic Event Classification (AEC) has been widely used in devices such as smart speakers and mobile phones for home safety or accessibility support. As AEC models run on more and more devices with diverse computation resource constraints, it became
Externí odkaz:
http://arxiv.org/abs/2303.10351
Autor:
Feng, Meng, Kao, Chieh-Chi, Tang, Qingming, Sun, Ming, Rozgic, Viktor, Matsoukas, Spyros, Wang, Chao
Standard acoustic event classification (AEC) solutions require large-scale collection of data from client devices for model optimization. Federated learning (FL) is a compelling framework that decouples data collection and model training to enhance c
Externí odkaz:
http://arxiv.org/abs/2203.11997
Autor:
Kao, Chieh1,2 (AUTHOR) ed101397@edah.org.tw, Wang, Shih-Wei2,3 (AUTHOR) yufeng528@gmail.com, Chen, Po-Chun4 (AUTHOR) pcchen@ntnu.edu.tw, Huang, Chun-Yung5 (AUTHOR) cyhuang@nkust.edu.tw, Wei, Yu-Feng2,6 (AUTHOR), Ho, Cheng-Hsun1 (AUTHOR) chenghsunho@gmail.com, Hong, Yong-Han7 (AUTHOR) yonghan@ntnu.edu.tw
Publikováno v:
International Journal of Molecular Sciences. Sep2024, Vol. 25 Issue 18, p10133. 15p.
Autor:
Wu, Ho-Hsiang, Kao, Chieh-Chi, Tang, Qingming, Sun, Ming, McFee, Brian, Bello, Juan Pablo, Wang, Chao
Deep learning is very data hungry, and supervised learning especially requires massive labeled data to work well. Machine listening research often suffers from limited labeled data problem, as human annotations are costly to acquire, and annotations
Externí odkaz:
http://arxiv.org/abs/2102.03229
Autor:
Gao, Yixin, Stein, Noah D., Kao, Chieh-Chi, Cai, Yunliang, Sun, Ming, Zhang, Tao, Vitaladevuni, Shiv
Wake word (WW) spotting is challenging in far-field due to the complexities and variations in acoustic conditions and the environmental interference in signal transmission. A suite of carefully designed and optimized audio front-end (AFE) algorithms
Externí odkaz:
http://arxiv.org/abs/2010.06676
Knowledge Distillation (KD) is a popular area of research for reducing the size of large models while still maintaining good performance. The outputs of larger teacher models are used to guide the training of smaller student models. Given the repetit
Externí odkaz:
http://arxiv.org/abs/2009.01759
This paper proposes a network architecture mainly designed for audio tagging, which can also be used for weakly supervised acoustic event detection (AED). The proposed network consists of a modified DenseNet as the feature extractor, and a global ave
Externí odkaz:
http://arxiv.org/abs/2008.03350
We study few-shot acoustic event detection (AED) in this paper. Few-shot learning enables detection of new events with very limited labeled data. Compared to other research areas like computer vision, few-shot learning for audio recognition has been
Externí odkaz:
http://arxiv.org/abs/2002.09143
Acoustic event classification (AEC) and acoustic event detection (AED) refer to the task of detecting whether specific target events occur in audios. As long short-term memory (LSTM) leads to state-of-the-art results in various speech related tasks,
Externí odkaz:
http://arxiv.org/abs/2002.06279
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