Year of Publication
Master of Science in Electrical Engineering (MSEE)
Electrical and Computer Engineering
Dr. Michael T. Johnson
Dr. Kevin Donohue
Acoustic analysis of animal vocalizations has been widely used to identify the presence of individual species, classify vocalizations, identify individuals, and determine gender. In this work automatic identification of speaker and gender of mice from ultrasonic vocalizations and speaker identification of meerkats from their Close calls is investigated. Feature extraction was implemented using Greenwood Function Cepstral Coefficients (GFCC), designed exclusively for extracting features from animal vocalizations. Mice ultrasonic vocalizations were analyzed using Gaussian Mixture Models (GMM) which yielded an accuracy of 78.3% for speaker identification and 93.2% for gender identification. Meerkat speaker identification with Close calls was implemented using Gaussian Mixture Models (GMM) and Hidden Markov Models (HMM), with an accuracy of 90.8% and 94.4% respectively. The results obtained shows these methods indicate the presence of gender and identity information in vocalizations and support the possibility of robust gender identification and individual identification using bioacoustic data sets.
Digital Object Identifier (DOI)
Jose, Neenu, "SPEAKER AND GENDER IDENTIFICATION USING BIOACOUSTIC DATA SETS" (2018). Theses and Dissertations--Electrical and Computer Engineering. 120.
Available for download on Monday, June 08, 2020