Probabilistic Music-Symbol Spotting in Handwritten Scores
Jorge Calvo-Zaragoza, Alejandro Héctor Toselli, Enrique Vidal · 2018
Content-based search on musical manuscripts is usually performed assuming that there are accurate transcripts of the sources in a symbolic, structured format. Given that current systems for Handwritten Music Recognition are far from offering guarantees about their accuracy, this traditional approach does not represent a scalable scenario. In this work we propose a probabilistic framework for Music-Symbol Spotting (MSS), that allows for content-based music search directly over the images of the manuscripts. By means of statistical recognition systems, a probabilistic index is built upon which the search can be carried out efficiently. Our experiments over a dataset of an Early handwritten music manuscript in Mensural notation demonstrates that this MSS framework can be presented as a promising alternative to the traditional approach for content-based music search.