Can Style be Learned? A Machine Learning Approach Towards ‘Performing’ as Famous Pianists

In this paper a novel method for performing music in the style of famous pianists is presented. We use Kernel Canonical Correlation Analysis (KCCA), a method which looks for a common semantic representation between two views, to learn the correlation between a representation of a musical score and a representation of an artist’s performance of that score. We use the performance representation based on the variations of beat level global loudness and tempo through time, as suggested by [3]. Therefore, the crux of the matter is the representation of the musical scores and by implication a similarity measure between relevant features that capture our prior knowledge of music. We therefore proceed to propose a novel kernel for musical scores, which is a Gaussian kernel adaptation to the distances between rhythm patterns, melodic contours and chords progressions.