Real-time classification via sparse representation in acoustic sensor networks

Wei, Bo and Yang, Mingrui and Shen, Yiran and Rana, Rajib and Chou, Chun Tung and Hu, Wen (2013) Real-time classification via sparse representation in acoustic sensor networks. In: 11th ACM Conference on Embedded Networked Sensor Systems (SenSys 2013), 11-15 Nov 2013, Rome, Italy.

[img]
Preview
Text (Published Version)
wei_sensys13.pdf

Download (3471Kb) | Preview

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.


Statistics for USQ ePrint 28907
Statistics for this ePrint Item
Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org.
Faculty / Department / School: Current - Institute for Resilient Regions
Date Deposited: 05 Sep 2016 00:54
Last Modified: 23 Jan 2018 06:07
Uncontrolled Keywords: Acoustic Sensor Networks (ASNs); Audio classification; Sparse approximation
Fields of Research : 08 Information and Computing Sciences > 0805 Distributed Computing > 080504 Ubiquitous Computing
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1145/2517351.2517357
URI: http://eprints.usq.edu.au/id/eprint/28907

Actions (login required)

View Item Archive Repository Staff Only