Calvo-Zaragoza, Jorge, Oncina, Jose
Recognition of pen-based music notation with finite-state machines
Expert Systems with Applications. 2017, 72: 395-406. doi:10.1016/j.eswa.2016.10.041
URI: http://hdl.handle.net/10045/64374
DOI: 10.1016/j.eswa.2016.10.041
ISSN: 0957-4174 (Print)
Abstract: 
This work presents a statistical model to recognize pen-based music compositions using stroke recognition algorithms and finite-state machines. The series of strokes received as input is mapped onto a stochastic representation, which is combined with a formal language that describes musical symbols in terms of stroke primitives. Then, a Probabilistic Finite-State Automaton is obtained, which defines probabilities over the set of musical sequences. This model is eventually crossed with a semantic language to avoid sequences that does not make musical sense. Finally, a decoding strategy is applied in order to output a hypothesis about the musical sequence actually written. Comprehensive experimentation with several decoding algorithms, stroke similarity measures and probability density estimators are tested and evaluated following different metrics of interest. Results found have shown the goodness of the proposed model, obtaining competitive performances in all metrics and scenarios considered.
Keywords:Pen-based recognition, Optical music recognition, Finite-State machines
Elsevier
info:eu-repo/semantics/article