Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  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 tlve 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 vols 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%.
['Mark A. Bartsch', 'Gregory H. Wakefield']
I am writing a research paper on a idea Singing voice identification using spectral envelope estimation
[{"from": "human", "value": "I am writing a research paper on a idea Singing voice identification using spectral envelope estimation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  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 tlve 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 vols 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%."}]