In:
The Journal of the Acoustical Society of America, Acoustical Society of America (ASA), Vol. 120, No. 5_Supplement ( 2006-11-01), p. 3139-3139
Abstract:
Identification of acoustic signals in noisy environments remains one of the most difficult of signal processing problems, and is a major obstacle to the high degree of accuracy and speed needed to identify suspicious sounds in high-security, high-safety environments. We have previously developed an acoustic recognition capability using a novel, biologically based Dynamic Synapse Neural Network (DSNN) technology. The DSNN-based technology has been demonstrated to classify target sounds with a high degree of accuracy, even in high noise conditions. In this paper we focus on extending the acoustic recognition capability of DSNNs to the problem of gunshot recognition. In order to recognize and localize the event, an array of four microphones is used for sound input. For localization purpose, time-delay estimation algorithm (TDE) is employed for triangulation. We have developed stand-alone, portable, and cost-efficient hardware with which both recognition and localization can be performed. In field-testing, the system classifies and localizes over 90% of the trained-for sounds. Performance for firecracker, starter pistol, 9-mm, and 44-caliber, explosion/firing sounds was also tested.
Type of Medium:
Online Resource
ISSN:
0001-4966
,
1520-8524
Language:
English
Publisher:
Acoustical Society of America (ASA)
Publication Date:
2006
detail.hit.zdb_id:
1461063-2
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