NDLI: North Atlantic right whale acoustic signal processing: Part II. improved decision architecture for auto-detection using multi-classifier combination methodology

Content Provider	IEEE Xplore Digital Library
Author	Dugan, P.J. Rice, A.N. Urazghildiiev, I.R. Clark, C.W.
Copyright Year	2010
Description	Author affiliation: Bioacoustics Research Program, Cornell Laboratory of Ornithology, Cornell University, Ithaca, NY 14850 USA (Dugan, P.J.; Rice, A.N.; Urazghildiiev, I.R.; Clark, C.W.)
Abstract	Autonomous signal detection of the North Atlantic right whale (NRW), Eubalaena glacialis, is becoming an important factor in monitoring and conservation for this highly endangered species. Both online and offline systems exist to help study and protect animals within this population. In both cases auto-detection of species-specific calls plays a vital role in localizing individual animal by searching time-frequency passive acoustic data. This research presents an experimental system, referred to as the NRW-CRITIC, for automatic detection of the NRW contact call. In general, the CRITIC uses a combinatorial classifier approach to integrate a series of existing machine learning algorithms; each designed specifically for NRW contact call identification. The proposed configuration consists of several recognition methods running in parallel; these include linear discriminant analysis, artificial neural network (NET) and classification regression tree (CART). This paper presents the details for the NRW-CRITIC and discusses the approach used to combine multiple independent decisions into a single result. A side-by-side performance comparison, between the CRITIC and a well-known method, the feature vector testing (FVT), is summarized. Performance metrics are evaluated based on a large database of acoustic recordings consisting of over 58,000 NRW contact calls from various locations, including two critical habitats, Great South Channel and Cape Cod Bay. Results indicate the FVT algorithm yields a 74.7% detection probability with an error rate of 4.35%. In comparison the CRITIC, operating at similar information level yields a 78.02% detection probability with a 3.25% error rate, exceeding the performance of the FVT. Performance was also measured using data from a multi-channel acoustic array located in Massachusetts Bay. A side-by-side comparison of array presence is discussed for two separate days. Results show that with the FVT and CRITIC operating at 0% error for array presence, the FVT method had 18,769 and 24,469 false positives for the Massachusetts Bay datasets respectively. With the same 0% error condition the CRITIC provided successful detection with significantly lower number of false positive rates: 1,072 and 2,324 calls, respectively. Future extensions of this experimental work are also discussed.
Starting Page	1
Ending Page	6
File Size	988056
Page Count	6
File Format	PDF
ISBN	9781424455485
e-ISBN	9781424455508
DOI	10.1109/LISAT.2010.5478287
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Whales Acoustic signal processing Acoustic signal detection Animals Error analysis Signal detection Monitoring Protection Time frequency analysis Machine learning algorithms Multi-Classifier Right Whale Acoustic Monitoring Automated Detection
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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