Exploring vibrato-motivated acoustic features for singer identification

Vibrato is a slightly tremulous effect imparted to vocal or instrumental tone for added warmth and expressiveness through slight variation in pitch. It corresponds to a periodic fluctuation of the fundamental frequency. It is common for a singer to develop a vibrato function to personalize his/her singing style. In this paper, we explore the acoustic features that reflect vibrato information in order to identify singers of popular music. We start with an enhanced vocal detection method that allows us to select vocal segments with high confidence. From the selected vocal segments, the cepstral coefficients which reflect the vibrato characteristics are computed. These coefficients are derived using bandpass filters, such as parabolic and cascaded bandpass filters, spread according to the octave frequency scale. The strategy of our classifier formulation is to utilize the high level musical knowledge of song structure in singer modeling. Singer identification is validated on a database containing 84 popular songs from commercially available CD recordings from 12 singers. We achieve an average error rate of 16.2% in segment level identification.