Evaluation of a Sparse Representation-Based Classifier For Bird Phrase Classification Under Limited Data Conditions

摘要

This paper evaluates the performance of a sparse representation-based (SR) classifier for a limited data, bird phrase classification task. The evaluation database contains 32 unique phrases segmented from songs of the Cassin's Vireo (Vireo cassinii). Spectrographic features were extracted from each phrase-segmented audio file, followed by dimension reduction using principal component analysis (PCA). A performance comparison to the nearest subspace (NS) and support vector machine (SVM) classifiers was conducted. The SR classifier outperforms the NS and SVM classifiers, with a maximum absolute improvement of 3.4% observed when there are only four tokens per phrase in the training set.