Application of the Bayesian probability network to music scene analysis
Computational auditory scene analysis
A maximum likelihood approach to single-channel source separation
The Journal of Machine Learning Research
Neural networks for blind-source separation of Stromboli explosion quakes
IEEE Transactions on Neural Networks
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This work aims to propose a novel model to perform automatic music transcription of polyphonic audio sounds. 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 (waveforms) are classified by using a dictionary (i.e. database). 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 waveforms 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, real songs and recordings obtained in a real environment.