title: Sparse representation for classification of dolphin whistles by type.
abstract: A compressive-sensing approach called Sparse Representation Classifier (SRC) is applied to the classification of bottlenose dolphin whistles by type. The SRC algorithm constructs a dictionary of whistles from the collection of training whistles. In the classification phase, an unknown whistle is represented sparsely by a linear combination of the training whistles and then the call class can be determined with an l1-norm optimization procedure. Experimental studies conducted in this research reveal the advantages and limitations of the proposed method against some existing techniques such as K-Nearest Neighbors and Support Vector Machines in distinguishing different vocalizations.
authors.affiliation: Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, Florida 33431 mesfahan@fau.edu, zhuang@fau.edu, erdol@fau.edu.
authors.forename: M
authors.initials: M
authors.lastname: Esfahanian
authors.affiliation: Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, Florida 33431 mesfahan@fau.edu, zhuang@fau.edu, erdol@fau.edu.
authors.forename: H
authors.initials: H
authors.lastname: Zhuang
authors.affiliation: Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, Florida 33431 mesfahan@fau.edu, zhuang@fau.edu, erdol@fau.edu.
authors.forename: N
authors.initials: N
authors.lastname: Erdol
mesh_terms: D000162:Acoustics; D000465:Algorithms; D000818:Animals; D004289:Dolphins / Q000145:classification* / Q000502:physiology*; D016014:Linear Models; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography; D013045:Species Specificity; D060388:Support Vector Machine; D013997:Time Factors; D014828:Vocalization, Animal