This work aims to propose a novel model to perform automatic music transcription of polyphonic audio data. The notes of different musical instruments are extracted from a single channel recording by using a non-linear Principal Component Analysis Neural Network. The estimated components are associated to different instruments considering a dictionary (i.e. database system). The dictionary contains the features of the notes for several musical instruments (i.e. probability densities). A Kullback-Leibler divergence is used to recognize the extract sources as belonging to one instrument in the database. Moreover, considering the weights of the Neural Network a MUSIC frequency estimator is used to obtain the frequencies of the musical notes. Several results are proposed to show the performance of this technique for the transcription of mixtures of different musical instruments, of real songs and recordings obtained in a real environment.

Single Channel Polyphonic Music Transcription

CIARAMELLA, Angelo
2008

Abstract

This work aims to propose a novel model to perform automatic music transcription of polyphonic audio data. The notes of different musical instruments are extracted from a single channel recording by using a non-linear Principal Component Analysis Neural Network. The estimated components are associated to different instruments considering a dictionary (i.e. database system). The dictionary contains the features of the notes for several musical instruments (i.e. probability densities). A Kullback-Leibler divergence is used to recognize the extract sources as belonging to one instrument in the database. Moreover, considering the weights of the Neural Network a MUSIC frequency estimator is used to obtain the frequencies of the musical notes. Several results are proposed to show the performance of this technique for the transcription of mixtures of different musical instruments, of real songs and recordings obtained in a real environment.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11367/25317
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