Methodology for automatic bioacoustic classification of anurans based on feature fusion |
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| Affiliation: | 1. Institute for Technological Development and Innovation in Communications, Spain;2. Signal and Communications Department, Spain;3. Telematic Engineering Department, University of Las Palmas de Gran Canaria, Campus Universitario de Tafira S/N, 35017 Las Palmas de Gran Canaria, Spain;1. Department of Electronics and Communication Engineering, RCC Institute of Information Technology, Kolkata 700015, India;2. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata 700108, India;3. Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata 700032, India;1. Departamento de Economía Aplicada y Métodos Cuantitativos, Universidad de La Laguna, 38071 La Laguna, Spain;2. Departamento de Ingeniería Informática y de Sistemas, Universidad de La Laguna, 38071 La Laguna, Spain;1. Grado Department of Industrial and Systems Engineering, System Performance Laboratory, Virginia Tech, Falls Church, VA 22043, USA;2. Business Analytics & Statistics Department, Haslam College of Business, The University of Tennessee, Knoxville, TN 37996-0532, USA;3. Centre for Management of Technology and Entrepreneurship, University of Toronto, 200 College Street, Toronto, Ontario M5S 3E5, Canada;4. Rogers Communications Inc., Toronto, Ontario M4W 1G9, Canada;1. Department of Aerospace Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States;2. Department of Pediatrics-Division of Pediatric Endocrinology, University of Michigan Health System, Ann Arbor, Michigan 48109, United States;3. Department of Radiology, Section of Pediatric Radiology University of Michigan Health System, Ann Arbor, Michigan 48109, United States;1. Center of Electronics, Communications and Information Technology, Admiral Wandenkolk Instruction Center, Brazilian Navy, Rio de Janeiro, RJ, Brazil;2. Department of Electronics Engineering and Telecommunication, Engineering Faculty, State University of Rio de Janeiro, RJ, Brazil;3. Department of System Engineering and Computation, Engineering Faculty, State University of Rio de Janeiro, RJ, Brazil |
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| Abstract: | The automatic recognition of anurans by their calls provides indicators of ecosystem health and habitat quality. This paper presents a new methodology for the acoustic classification of anurans using a fusion of frequency domain features, Mel and Linear Frequency Cepstral Coefficients (MFCCs and LFCCs), with time domain features like entropy and syllable duration through intelligent systems. This methodology has been validated in three databases with a significant number of different species proving the strength of this approach. First, the audio recordings are automatically segmented into syllables which represent different anuran calls. For each syllable, both types of features are computed and evaluated separately as in previous works. In the experiments, a novel data fusion method has been used showing an increase of the classification accuracy which achieves an average of 98.80% ± 2.43 in 41 anuran species from AmphibiaWeb database, 96.90% ± 3.57 in 58 frogs from Cuba and 95.48% ± 4.97 in 100 anurans from southern Brazil and Uruguay; reaching a classification rate of 95.38% ± 5.05 for the aggregate dataset of 199 species. |
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