Abstract
Acoustic scene analysis can be used to extract relevant information in applications such as homeland security, surveillance and environmental monitoring. Wireless sensor networks have been of particular interest in monitoring acoustic scenes. Sensors embedded in such a network typically operate under several constraints such as low power and limited bandwidth. In this paper, we consider resource-efficient acoustic sensing tasks that extract and transmit relevant information to a central station where information assessment can be conducted. We propose a series of acoustic scene analysis tasks that are performed in a hierarchical manner. Hierarchical tasks include sound and speech discrimination, estimation of the number of speakers from the acquired sound, gender and emotional state, and ultimately voice monitoring and key word spotting. We apply support vector machine and Gaussian mixture model algorithms on sound features. A real-time implementation is accomplished using Crossbow motes interfaced with a TI DSP board. A series of experiments are presented to characterize the performance of the algorithms under different conditions.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - IEEE International Symposium on Circuits and Systems |
| Pages | 169-172 |
| Number of pages | 4 |
| DOIs | |
| State | Published - 2009 |
| Event | 2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009 - Taipei, Taiwan, Province of China Duration: May 24 2009 → May 27 2009 |
Other
| Other | 2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009 |
|---|---|
| Country | Taiwan, Province of China |
| City | Taipei |
| Period | 5/24/09 → 5/27/09 |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
Cite this
A sensor network for real-time acoustic scene analysis. / Kwon, Homin; Krishnamoorthi, Harish; Berisha, Visar; Spanias, Andreas.
Proceedings - IEEE International Symposium on Circuits and Systems. 2009. p. 169-172 5117712.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A sensor network for real-time acoustic scene analysis
AU - Kwon, Homin
AU - Krishnamoorthi, Harish
AU - Berisha, Visar
AU - Spanias, Andreas
PY - 2009
Y1 - 2009
N2 - Acoustic scene analysis can be used to extract relevant information in applications such as homeland security, surveillance and environmental monitoring. Wireless sensor networks have been of particular interest in monitoring acoustic scenes. Sensors embedded in such a network typically operate under several constraints such as low power and limited bandwidth. In this paper, we consider resource-efficient acoustic sensing tasks that extract and transmit relevant information to a central station where information assessment can be conducted. We propose a series of acoustic scene analysis tasks that are performed in a hierarchical manner. Hierarchical tasks include sound and speech discrimination, estimation of the number of speakers from the acquired sound, gender and emotional state, and ultimately voice monitoring and key word spotting. We apply support vector machine and Gaussian mixture model algorithms on sound features. A real-time implementation is accomplished using Crossbow motes interfaced with a TI DSP board. A series of experiments are presented to characterize the performance of the algorithms under different conditions.
AB - Acoustic scene analysis can be used to extract relevant information in applications such as homeland security, surveillance and environmental monitoring. Wireless sensor networks have been of particular interest in monitoring acoustic scenes. Sensors embedded in such a network typically operate under several constraints such as low power and limited bandwidth. In this paper, we consider resource-efficient acoustic sensing tasks that extract and transmit relevant information to a central station where information assessment can be conducted. We propose a series of acoustic scene analysis tasks that are performed in a hierarchical manner. Hierarchical tasks include sound and speech discrimination, estimation of the number of speakers from the acquired sound, gender and emotional state, and ultimately voice monitoring and key word spotting. We apply support vector machine and Gaussian mixture model algorithms on sound features. A real-time implementation is accomplished using Crossbow motes interfaced with a TI DSP board. A series of experiments are presented to characterize the performance of the algorithms under different conditions.
UR - http://www.scopus.com/inward/record.url?scp=70350158541&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70350158541&partnerID=8YFLogxK
U2 - 10.1109/ISCAS.2009.5117712
DO - 10.1109/ISCAS.2009.5117712
M3 - Conference contribution
AN - SCOPUS:70350158541
SN - 9781424438280
SP - 169
EP - 172
BT - Proceedings - IEEE International Symposium on Circuits and Systems
ER -