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In this paper, we present a spectrum-based system for singer identification that operates for the ideal case in which audio samples contain only the singer's voice. Our method begins with the computation of a robust estimate of the spectral envelope called the composite transfer function (CTF). The CTF is derived from the instantaneous amplitude and frequency of the sinusoidal partials which make up the vocal signal. Unlike traditional source-filter theory , the CTF does not explicitly separate the spectral characteristics of the vocal source and the vocal tract filter. The principal components of the CTFs are used as features for a quadratic classifier to identify singers. The approach is validated on a database containing samples from twelve classically trained singers. In cross validation experiments, test set accuracies of approximately 95% are found for a baseline case. The classifier's performance is not degraded when different vowels are included in classifier training and evaluation. Restricting the frequency range of the CTFs and using a test set containing samples extracted from solo performances of Italian arias reduces the test set accuracy to 70-80%.