NDLI: Detecting vocalizations of individual monkeys in social groups

Content Provider	IEEE Xplore Digital Library
Author	Bayestehtashk, A. Shafran, I. Coleman, K. Robertson, N.
Copyright Year	2014
Description	Author affiliation: Oregon Nat. Primate Res. Center, Oregon Health & Sci. Univ., Portland, OR, USA (Coleman, K.; Robertson, N.) \|\| Center for Spoken Language Understanding, Oregon Health & Sci. Univ., Portland, OR, USA (Bayestehtashk, A.; Shafran, I.)
Abstract	Vocalization is an important clue in recognizing monkeys' behaviors. Previous studies have shown that the frequencies, the types and the lengths of vocalizations reveal significant information about social interactions in a group of monkeys. In this work, we describe a corpus of monkey vocalizations, recorded from Oregon National Primate Research Center with the goal of developing automatic methods for recognizing social behaviors of individuals in groups. The constraints of the problem necessitated using tiny low-power recorders, mounted on their collars. The recordings from each monkeys' recorder nonetheless contains vocalizations from not only the monkey wearing the recorder but also its spatial neighbors. The devices recorded vocalizations for two consecutive days, 12 hours each day, from each monkey in the group. Like in sensor networks, low power recorders are unreliable and have sample loss over long durations. Furthermore, the recordings contain high-levels of background noise, including clanging of metal collars against cages and conversations of caretakers. These practical issues poses an interesting challenge in processing the recordings. In this paper, we investigate our automated approaches to process the data efficiently, detect the vocalizations and align the recordings from the same sessions.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	4775
Ending Page	4779
File Size	4767122
Page Count	5
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6944692
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Noise measurement Noise Animals Accuracy Standards Audio recording
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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

Abnormality detection in noisy biosignals

What does clean EEG look like?

On the objective assessment of the auditory brainstem response measurement quality

Detection of breathing sounds during sleep using non-contact audio recordings

Morphological processing of physiological signals for feature extraction

Investigating deep learning for fNIRS based BCI

Arousal recognition system based on heartbeat dynamics during auditory elicitation

Vision-based absence seizure detection

Scleroderma capillary pattern identification using texture descriptors and ensemble classification

Detecting vocalizations of individual monkeys in social groups

Similar Documents

Abnormality detection in noisy biosignals

What does clean EEG look like?

On the objective assessment of the auditory brainstem response measurement quality

Detection of breathing sounds during sleep using non-contact audio recordings

Morphological processing of physiological signals for feature extraction

Investigating deep learning for fNIRS based BCI

Arousal recognition system based on heartbeat dynamics during auditory elicitation

Vision-based absence seizure detection

Scleroderma capillary pattern identification using texture descriptors and ensemble classification

Detecting vocalizations of individual monkeys in social groups