A Parse-based Framework for Coupled Rhythm Quantization and Score Structuring

International audienceWe present a formal language-based framework for MIDI-to-score transcription, the problem of converting a sequence of symbolic musical events with arbitrary timestamps into a structured music score. The framework aims at solving in one pass the two subproblems of rhythm quantization and score production. It relies, throughout the process, on an apriori hierarchical model of scores given by generative grammars. We show that this coupled approach helps to make relevant and interrelated decisions, and we present an algorithm computing transcription solutions optimal with respect to both the fitness of the quantization to the input, and a measure of complexity of music notation

A diff procedure for music score files: Computation and visualization of the differences between two music score files

International audienceComparing music score files is an important task for many activities such as collaborative score editing, version control and evaluation of optical music recognition (OMR) or music transcription. Following the Unix diff model for text files, we propose an original procedure for computing the differences between two score files, typically in XML format. It performs a comparison of scores at the notation (graphical) level, based on a new intermediate tree representation of the music notation content of a score and a combination of sequence- and tree-edit distances. We also propose a tool to visualize the differences between two scores side-by-side, using the music notation engraving library Verovio, and we employ it to test the procedure on an OMR dataset

Modeling and Learning Rhythm Structure

International audienceWe present a model to express preferences on rhythmic structure, based on probabilistic context-free grammars, and a procedure that learns the grammars probabilities from a dataset of scores or quantized MIDI files. The model formally defines rules related to rhythmic subdivisions and durations that are in general given in an informal language. Rules preference is then specified with probability values. One targeted application is the aggregation of rules probabilities to qualify an entire rhythm, for tasks like automatic music generation and music transcription. The paper also reports an application of this approach on two datasets