- Pertusa A., Iņesta, J.M.
"Polyphonic monotimbral music transcription using dynamic networks"
Pattern Recognition Letters, vol. 26, pp. 1809-1818
The automatic extraction of the notes that were played in a digital musical signal (automatic music transcription) is an open problem. A number of techniques have been applied to solve it without concluding results. The monotimbral polyphonic version of the problem is posed here: a single instrument has been played and more than one note can sound at the same time. This work tries to approach it through the identification of the pattern of a given instrument in the frequency domain. This is achieved using time-delay neural networks that are fed with the band-grouped spectrogram of a polyphonic monotimbral music recording. The use of a learning scheme based on examples like neural networks permits our system to avoid the use of an auditory model to approach this problem. A number of issues have to be faced to have a robust and powerful system, but promising results using synthesized instruments are presented.
author = "Pertusa A., Iņesta, J.M.",
title = "Polyphonic monotimbral music transcription using dynamic networks",
issn = "0167-8655",
journal = "Pattern Recognition Letters",
month = "september",
number = "12",
pages = "1809-1818",
volume = "26",
year = "2005"