Vibrato-Motivated Acoustic Features for Singger Identification

It is common that a singer develops a vibrato to personalize his/her singing style. In this paper, we explore the acoustic features that reflect vibrato information, 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 cascaded bandpass filters spread according to the octave frequency scale. We employ the high level musical knowledge of song structure in singer modeling. Singer identification is validated on a database containing 84 popular songs in commercially available CD records from 12 singers. We achieve an average error rate of 16.2% in segment level identification

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