Polyphonic Piano Transcription Based on Spectral Separation
We propose a discriminative model for polyphonic piano transcription. Spectral features are obtained individually for each note. To solve the overlapping partial problem, we apply spectral separation by estimating the spectral envelope for each note. For classifying purposes, support vector machines (SVM) are trained on the spectral energy inferred from these spectral features. We apply a scheme of one-versus-all (OVA) SVM classiﬁers to discriminate frame-level note instances. To decrease the high frequency notes residual energy due to the downward notes shared partials, a method to cancel the interferences from the downward notes to the upward notes has been developed. The classiﬁer output is ﬁltered with a hidden Markov model. Our approach has been tested with synthesized and real piano recordings obtaining very promising results.
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