+ General Information
+ Members
+ Research
+ Intranet

PRAIg '16 / +pics


9th international workshop on Machine Learning and Music


Riva del Garda (Italy): 19th - 23rd September 2016



  1. Pertusa A., Iñesta, J.M.
    "Polyphonic monotimbral music transcription using dynamic networks"
    Pattern Recognition Letters, vol. 26, pp. 1809-1818 (2005)
    : bibtex : pdf

    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.

@article {
 author = "Pertusa A., Iñesta, 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"
Valid XHTML 1.0!Valid CSS!