Automatic classification and speaker identification of African elephant (Loxodonta africana) vocalizations.
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.
Clemins
Patrick J
PJ
Speech and Signal Processing Laboratory, Marquette University, Milwaukee, Wisconsin 53233-1881, USA. patrick.clemins@marquette.edu
Johnson
Michael T
MT
Leong
Kirsten M
KM
Savage
Anne
A
D000162:Acoustics; D000823:Animal Identification Systems / Q000145:classification; D000818:Animals; D004606:Elephants; D005260:Female; D005583:Fourier Analysis; D008297:Male; D008390:Markov Chains; D010700:Phonetics; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography / Q000145:classification* / Q000706:statistics & numerical data; D013061:Speech Acoustics; D014828:Vocalization, Animal / Q000145:classification*