A sparse representation-based classifier for in-set bird phrase verification and classification with limited training data

The performance of a sparse representation-based (SR) classifier for in-set bird phrase verification and classification is studied. The database contains phrases segmented from songs of the Cassin´s Vireo (Vireo cassinii). Each test phrase belongs to one of 33 phrase classes - 32 in-set categories, and 1 collective out-of-set category. Only in-set phrases are used for training. From each phrase segment, spectrographic features were extracted, followed by dimension reduction using PCA. A threshold is applied on the sparsity concentration index (SCI) computed by the SR classifier, for in-set bird phrase verification using a limited number of training tokens (3 - 7) per phrase class. When evaluated against the nearest subspace (NS) and support vector machine (SVM) classifiers using the same framework, the SR classifier has the highest classification accuracy, due to its good performances in both the verification and classification tasks.