Audio Event Detection Using Wireless Sensor Networks Based on Deep Learning

Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 264)


Wireless acoustic sensor network is useful for ambient assisted living applications. Its capability of incorporating an audio event detection and classification system helps its users, especially elderly, on their everyday needs. In this paper, we propose using convolutional neural networks (CNN) for classifying audio streams. In contrast to AAL systems using traditional machine learning, our solution is capable of learning and inferring activities in an end-to-end manner. To demonstrate the system, we developed a wireless sensor network composed of Raspberry Pi boards with microphones as nodes. The audio classification system results to an accuracy of 83.79% using a parallel network for the Urban8k dataset, extracting constant-Q transform (CQT) features as system inputs. The overall system is scalabale and flexible in terms of the number of nodes, hence it is applicable on wide areas where assisted living applications are utilized.


Audio event detection Ambient assisted living 



The authors would like to acknowledge the support of the University of the Philippines Diliman and the Department of Science and Technology through the Engineering Research and Development for Technology (ERDT) Consortium.


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.Electrical and Electronics Engineering InstituteUniversity of the Philippines - DilimanQuezon CityPhilippines

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