Real-time classification via sparse representation in acoustic sensor networks

Paper


Wei, Bo, Yang, Mingrui, Shen, Yiran, Rana, Rajib, Chou, Chun Tung and Hu, Wen. 2013. "Real-time classification via sparse representation in acoustic sensor networks." 11th ACM Conference on Embedded Networked Sensor Systems (SenSys 2013). Rome, Italy 11 - 15 Nov 2013 United States. https://doi.org/10.1145/2517351.2517357
Paper/Presentation Title

Real-time classification via sparse representation in acoustic sensor networks

Presentation TypePaper
AuthorsWei, Bo (Author), Yang, Mingrui (Author), Shen, Yiran (Author), Rana, Rajib (Author), Chou, Chun Tung (Author) and Hu, Wen (Author)
Journal or Proceedings TitleSenSys 2013 - Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
ERA Conference ID42281
Number of Pages14
Year2013
Place of PublicationUnited States
ISBN9781450320276
Digital Object Identifier (DOI)https://doi.org/10.1145/2517351.2517357
Web Address (URL) of Paperhttp://dl.acm.org/citation.cfm?doid=2517351.2517357
Conference/Event11th ACM Conference on Embedded Networked Sensor Systems (SenSys 2013)
ACM Conference on Embedded Networked Sensor Systems
Event Details
ACM Conference on Embedded Networked Sensor Systems
SENSYS
Rank
A
A
A
A
A
A
A
A
A
A
Event Details
11th ACM Conference on Embedded Networked Sensor Systems (SenSys 2013)
Event Date
11 to end of 15 Nov 2013
Event Location
Rome, Italy
Abstract

Acoustic Sensor Networks (ASNs) have a wide range of applications in natural and urban environment monitoring, as well as indoor activity monitoring. In-network classification is critically important in ASNs because wireless transmission costs several orders of magnitude more energy than computation. The main challenges of in-network classification in ASNs include effective feature selection, intensive computation requirement and high noise levels. To address these challenges, we propose a sparse representation based feature-less, low computational cost, and noise resilient framework for in-network classification in ASNs. The key component of Sparse Approximation based Classification (SAC), ℓ1 minimization, is a convex optimization problem, and is known to be computationally expensive. Furthermore, SAC algorithms assumes that the test samples are a linear combination of a few training samples in the training sets. For acoustic applications, this results in a very large training dictionary, making the computation infeasible to be performed on resource constrained ASN platforms. Therefore, we propose several techniques to reduce the size of the problem, so as to fit SAC for in-network classification in ASNs. Our extensive evaluation using two real-life datasets (consisting of calls from 14 frog species and 20 cricket species respectively) shows that the proposed SAC framework outperforms conventional approaches such as Support Vector Machines (SVMs) and k-Nearest Neighbor (kNN) in terms of classification accuracy and robustness. Moreover, our SAC approach can deal with multi-label classification which is common in ASNs. Finally, we explore the system design spaces and demonstrate the real-time feasibility of the proposed framework by the implementation and evaluation of an acoustic classification application on an embedded ASN testbed.

KeywordsAcoustic Sensor Networks (ASNs); Audio classification; Sparse approximation
ANZSRC Field of Research 2020461199. Machine learning not elsewhere classified
Public Notes

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Byline AffiliationsUniversity of New South Wales
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
Institution of OriginUniversity of Southern Queensland
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