|Title||A Machine Learning Approach to Expressive Performance in Jazz Standards |
|Publication Type||Book Chapter |
|Year of Publication||2006 |
|Authors||Ramírez, R., Hazan A., Maestre E., & Serra X. |
|Editor||Petrushin, V. A., & Khan L. |
|Book Title||Multimedia Data Mining and Knowledge Discovery |
|Abstract||In this chapter we present a data mining approach to one of the most challenging aspects of computer music modeling the knowledge applied by a musician when performing a score in order to produce an expressive performance of a piece. We apply data mining techniques to real performance data (i.e. audio recordings) in order to induce an expressive performance model. This leads to an expressive performance system consisting of three components (1) a melodic transcription component, (2) a data mining component and (3) a melody synthesis component. We describe, explore and compare different data mining techniques for inducing the expressive transformation model.