Dynamic time warping and sparse representation classification for birdsong phrase classification using limited training data

The Journal of the Acoustical Society of America
Lee N TanCharles E Taylor

Abstract

Annotation of phrases in birdsongs can be helpful to behavioral and population studies. To reduce the need for manual annotation, an automated birdsong phrase classification algorithm for limited data is developed. Limited data occur because of limited recordings or the existence of rare phrases. In this paper, classification of up to 81 phrase classes of Cassin's Vireo is performed using one to five training samples per class. The algorithm involves dynamic time warping (DTW) and two passes of sparse representation (SR) classification. DTW improves the similarity between training and test phrases from the same class in the presence of individual bird differences and phrase segmentation inconsistencies. The SR classifier works by finding a sparse linear combination of training feature vectors from all classes that best approximates the test feature vector. When the class decisions from DTW and the first pass SR classification are different, SR classification is repeated using training samples from these two conflicting classes. Compared to DTW, support vector machines, and an SR classifier without DTW, the proposed classifier achieves the highest classification accuracies of 94% and 89% on manually segmented and automatically s...Continue Reading

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Citations

Aug 23, 2018·Journal of Biological Rhythms·Azure D GrantLance J Kriegsfeld
Dec 3, 2016·The Journal of the Acoustical Society of America·Kantapon KaewtipCharles E Taylor
Nov 3, 2017·The Journal of the Acoustical Society of America·Kun QianBjörn Schuller
Mar 17, 2019·Biology·Grace H GohDominique Blache
Apr 2, 2018·The Journal of the Acoustical Society of America·Long N Le, Douglas L Jones
Nov 14, 2019·Scientific Reports·Matthew T StampsAjay S Mathuru

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