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Robust audio sensing with multi-sound classification

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PerCom_2019_Sound.pdf (1.366Mb)
Date
22/07/2019
Author
Ye, Juan
Haubrick, Peter
Keywords
QA75 Electronic computers. Computer science
DAS
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Abstract
Audio data is a highly rich form of information, often containing patterns with unique acoustic signatures. In pervasive sensing environments, because of the empowered smart devices, we have witnessed an increasing research interest in sound sensing to detect ambient environment, recognise users' daily activities, and infer their health conditions. However, the main challenge is that the real-world environment often contains multiple sound sources, which can significantly compromise the robustness of the above environment, event, and activity detection applications. In this paper, we explore different approaches in multi-sound classification, and propose a stacked classifier based on the recent advance in deep learning. We evaluate our proposed approach in a comprehensive set of experiments on both sound effect and real-world datasets. The results have demonstrated that our approach can robustly identify each sound category among mixed acoustic signals, without the need of any a priori knowledge about the number and signature of sounds in the mixed signals.
Citation
Ye , J & Haubrick , P 2019 , Robust audio sensing with multi-sound classification . in 2019 IEEE International Conference on Pervasive Computing and Communications (PerCom) . , 8767402 , Pervasive Computing and Communications (PerCom) , IEEE Computer Society , pp. 1-7 , IEEE International Conference on Pervasive Computing and Communications (PerCom 2019) , Kyoto , Japan , 12/03/19 . https://doi.org/10.1109/PERCOM.2019.8767402
 
conference
 
Publication
2019 IEEE International Conference on Pervasive Computing and Communications (PerCom)
DOI
https://doi.org/10.1109/PERCOM.2019.8767402
ISSN
2474-2503
Type
Conference item
Rights
Copyright © 2019 IEEE. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1109/PERCOM.2019.8767402
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  • University of St Andrews Research
URI
http://hdl.handle.net/10023/18998

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