title: Compensating for the effects of site and equipment variation on delphinid species identification from their echolocation clicks.
abstract: A concern for applications of machine learning techniques to bioacoustics is whether or not classifiers learn the categories for which they were trained. Unfortunately, information such as characteristics of specific recording equipment or noise environments can also be learned. This question is examined in the context of identifying delphinid species by their echolocation clicks. To reduce the ambiguity between species classification performance and other confounding factors, species whose clicks can be readily distinguished were used in this study: Pacific white-sided and Risso's dolphins. A subset of data from autonomous acoustic recorders located at seven sites in the Southern California Bight collected between 2006 and 2012 was selected. Cepstral-based features were extracted for each echolocation click and Gaussian mixture models were used to classify groups of 100 clicks. One hundred Monte-Carlo three-fold experiments were conducted to examine classification performance where fold composition was determined by acoustic encounter, recorder characteristics, or recording site. The error rate increased from 6.1% when grouped by acoustic encounter to 18.1%, 46.2%, and 33.2% for grouping by equipment, equipment category, and site, respectively. A noise compensation technique reduced error for these grouping schemes to 2.7%, 4.4%, 6.7%, and 11.4%, respectively, a reduction in error rate of 56%-86%.
authors.affiliation: Department of Computer Science, San Diego State University, 5500 Campanile Drive, San Diego, California 92182-7720.
authors.forename: Marie A
authors.initials: MA
authors.lastname: Roch
authors.affiliation: Department of Computer Science, San Diego State University, 5500 Campanile Drive, San Diego, California 92182-7720.
authors.forename: Johanna
authors.initials: J
authors.lastname: Stinner-Sloan
authors.affiliation: Scripps Institution of Oceanography, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093-0205.
authors.forename: Simone
authors.initials: S
authors.lastname: Baumann-Pickering
authors.affiliation: Scripps Institution of Oceanography, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093-0205.
authors.forename: Sean M
authors.initials: SM
authors.lastname: Wiggins
mesh_terms: D000465:Algorithms; D000818:Animals; D004289:Dolphins / Q000145:classification / Q000502:physiology*; D004455:Echolocation / Q000145:classification; D005583:Fourier Analysis; D000069550:Machine Learning; D009010:Monte Carlo Method; D016011:Normal Distribution; D010140:Pacific Ocean; D010363:Pattern Recognition, Automated / Q000379:methods*; D013018:Sound Spectrography / Q000295:instrumentation / Q000379:methods*; D013045:Species Specificity; D013382:Subtraction Technique; D014159:Transducers