Extraction of Musical Motifs from Handwritten Music Score Images

Abstract

A musical motif represents a sequence of musical notes that can determine the identity of a composer or a music style. Musical motifs extraction is of great interest to musicologists to make critical studies of music scores. Musical motifs extraction can be solved by using a string mining algorithm when music data is represented as a sequence. When music data is initially produced in XML or MIDI format or can be converted into those standards, it can be automatically represented as a sequence of notes. So, in this work, starting from digitized images of music scores, our objective is twofold: first, we design a system able to generate musical sequences from handwritten music scores. To address this issue, one of the segmentation-free R-CNN models trained on musical data have been used to detect and recognize musical primitives that are next transcribed into XML sequences. Then, the sequences are processed by a computational model of musical motifs extraction algorithm called CSMA (Constrained String Mining Algorithm). The consistency and performances of the framework are then discussed according to the efficiency of the R-CNN ( Region-proposal Convolutional Neural Network) based recognition system through the estimation of misclassified primitives relating to the detailed account of detected motifs. The carried-out experiments of our complete pipeline show that it is consistent to find more than 70% of motifs with less than 20% of average detection/classification R-CNN errors