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In this paper, we propose to identify the singers of popular songs using vibrato characteristics and high level musical knowledge of song structure. The proposed framework starts with a vocal detection process followed by a hypothesis test for the vocal/nonvocal verification. This method allows us to select vocal segments of high confidence for singer identification. From the selected vocal segments, the cepstral coefficients which reflect the vibrato characteristics are computed using the parabola bandpass filters spread according to the music frequency scale. The strategy in our classifier formulation is to utilize the high level musical knowledge of song structure in singer modeling. The proposed framework is validated on a database containing 84 popular songs of commercially available CD records from 12 singers. We achieve an average error rate of 17.9% in segment level identification.