Document Type
Article
Language
eng
Format of Original
8 p.
Publication Date
2-2005
Publisher
Acoustical Society of America
Source Publication
Journal of the Acoustical Society of America
Source ISSN
0001-4966
Original Item ID
doi: 10.1121/1.1847850
Abstract
A hidden Markov model (HMM) system is presented for automatically classifying African elephant vocalizations. The development of the system is motivated by successful models from human speech analysis and recognition. Classification features include frequency-shifted Mel-frequency cepstral coefficients (MFCCs) and log energy, spectrally motivated features which are commonly used in human speech processing. Experiments, including vocalization type classification and speaker identification, are performed on vocalizations collected from captive elephants in a naturalistic environment. The system classified vocalizations with accuracies of 94.3% and 82.5% for type classification and speaker identification classification experiments, respectively. Classification accuracy, statistical significance tests on the model parameters, and qualitative analysis support the effectiveness and robustness of this approach for vocalization analysis in nonhuman species.
Recommended Citation
Clemins, Patrick J.; Johnson, Michael T.; Leong, Kirsten; and Savage, Anne, "Automatic Classification and Speaker Identification of African Elephant (Loxodonta africana) Vocalizations" (2005). Electrical and Computer Engineering Faculty Research and Publications. 40.
https://epublications.marquette.edu/electric_fac/40
Comments
Published version. Journal of the Acoustical Society of America, Vol. 117, No. 2 (February 2005): 956-963. DOI. © 2005 Acoustical Society of America. Used with permission.