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Erschienen in:
Improving Uplink Resource Batch Utilization in LTE Licensed-Assisted Access Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Audio Event Detection Using Wireless Sensor Networks Based on Deep Learning

verfasst von : Jose Marie Mendoza, Vanessa Tan, Jr. Vivencio Fuentes, Gabriel Perez, Nestor Michael Tiglao

Erschienen in: Wireless Internet

Verlag: Springer International Publishing

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Abstract

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.

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Vorheriges Kapitel Intrusion Detection for WiFi Network: A Deep Learning Approach
Nächstes Kapitel A Robust Remote Authentication Scheme for M-Commerce Environments
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Metadaten
Titel
Audio Event Detection Using Wireless Sensor Networks Based on Deep Learning
verfasst von
Jose Marie Mendoza
Vanessa Tan
Jr. Vivencio Fuentes
Gabriel Perez
Nestor Michael Tiglao
Copyright-Jahr
2019
Buch
Wireless Internet

Print ISBN: 978-3-030-06157-9

Electronic ISBN: 978-3-030-06158-6

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-06158-6_11

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