Cepstral coefficients and hidden Markov models reveal idiosyncratic voice characteristics in red deer (Cervus elaphus) stags

Abstract

(2006) Cepstral coefficients and hidden Markov models reveal idiosyncratic voice characteristics in red deer (Cervus elaphus) stags. Journal of the Acoustical Society of America, 120 (6). pp. [4080][4081][4082][4083][4084][4085][4086][4087][4088][4089] This version is available from Sussex Research Online: http://sro.sussex.ac.uk/756/ This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the URL above for details on accessing the published version. Copyright and reuse:Sussex Research Online is a digital repository of the research output of the University.Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable, the material made available in SRO has been checked for eligibility before being made available.Copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. Bouts of vocalizations given by seven red deer stags were recorded over the rutting period, and homomorphic analysis and hidden Markov models ͑two techniques typically used for the automatic recognition of human speech utterances͒ were used to investigate whether the spectral envelope of the calls was individually distinctive. Bouts of common roars ͑the most common call type͒ were highly individually distinctive, with an average recognition percentage of 93.5%. A "temporal" split-sample approach indicated that although in most individuals these identity cues held over the rutting period, the ability of the models trained with the bouts of roars recorded early in the rut to correctly classify later vocalizations decreased as the recording date increased. When Markov models trained using the bouts of common roars were used to classify other call types according to their individual membership, the classification results indicated that the cues to identity contained in the common roars were also present in the other call types. This is the first demonstration in mammals other than primates that individuals have vocal cues to identity that are common to the different call types that compose their vocal repertoire. Cepstral coefficients and hidden Markov models reveal idiosyncratic voice characteristics in red deer (Cervus elaphus) stags

Call recognition and individual identification of fish vocalizations based on automatic speech recognition: An example with the Lusitanian toadfish

“…Indeed the HMM system based on the feature set in Table III presented an average identification rate of 95.5%. This method reached values at least similar to or even higher than the identification rates observed in automatic recognition approaches with mammals' (Campbell et al, 2000;Clemins, 2005;Reby et al, 1997Reby et al, , 2006 and birds' (Terry and McGregor, 2002;Trawicki, 2005) vocalizations. The comparison, however, is not straightforward.…”

“…In such cases, the segmentation was not perfect resulting in apparent insertion errors that were in fact boatwhistles. As such, insertion errors were not considered in the evaluation of the recognition system (also see Parsons and Jones, 2000;Reby et al, 2006;Trifa et al, 2008).…”

FPGA Implementation of Blue Whale Calls Classifier Using High-Level Programming Tool

“…Analysis of underwater bioacoustic signal has been the object of numerous publications, where several approaches have been proposed to recognize and classify these signals [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Much like birds, marine mammals are highly vocalizing animals and different species can be recognized by their specific sounds [15].…”

Hidden Markov and Gaussian mixture models for automatic call classification

“…More recently HMMs have been used for classification or detection of vocalizations by African elephants Clemins and Johnson, 2006), red deer (Reby et al, 2006), and the ortolan bunting (Trawicki et al, 2005, Tao et al, 2008. There have been no computations with HMMs or GMMs on automatic classification of call types of marine mammal sounds.…”