Singer verification: Singer model .vs. song model

This paper proposes a method to verify the singer identity of a given song. The query song is modeled as a GMM learned on the features extracted from sustained sung notes of the song. Each note is described by the shape its spectral envelope and by the temporal variations in frequency and amplitude of its fundamental frequency. The singer identity is verified with two approaches: the model of the query song is compared to a singer-based GMM or compared to the GMM of another song performed by the same singer. The comparison is done using a dissimilarity measurement given by the Kullback Leibler divergence. When the two types of features are combined, the proposed approach verifies the singer identity of a given a cappella song with an error rate lower than 8% when the whole song is considered and an error rate lower than 10% when a short excerpt of the song (i.e. 15 consecutive sustained notes) is considered.