|Title||Understanding expressive transformations in saxophone jazz performances using inductive machine learning |
|Publication Type||Conference Paper |
|Year of Publication||2004 |
|Conference Name||Sound and Music Computing Conference |
|Authors||Ramírez, R., Hazan A., Gómez E., & Maestre E. |
|Abstract||In this paper, we describe an approach to learning expressive performance rules from monophonic Jazz standards recordings by a skilled saxophonist. We have first developed a melodic transcription system which extracts a set of acoustic features from the recordings producing a melodic representation of the expressive performance played by the musician. We apply machine learning techniques to this representation in order to induce rules of expressive music performance. It turns out that some of the induced rules represent extremely simple principles which are surprisingly general.
|Full Document||files/publications/smc04-RamirezHazanGomezMaestre.pdf |