Predicting Music Hierarchies with a Graph-Based Neural Decoder

This paper describes a data-driven framework to parse musical sequences into dependency trees, which are hierarchical structures used in music cognition research and music analysis. The parsing involves two steps. First, the input sequence is passed through a transformer encoder to enrich it with contextual information. Then, a classifier filters the graph of all possible dependency arcs to produce the dependency tree. One major benefit of this system is that it can be easily integrated into modern deep-learning pipelines. Moreover, since it does not rely on any particular symbolic grammar, it can consider multiple musical features simultaneously, make use of sequential context information, and produce partial results for noisy inputs. We test our approach on two datasets of musical trees -- time-span trees of monophonic note sequences and harmonic trees of jazz chord sequences -- and show that our approach outperforms previous methods.Comment: To be published in the Proceedings of the International Society for Music Information Retrieval Conference (ISMIR

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

Computation and Visualization of Differences between two XML Music Score Files

Late Breaking DemosInternational audienceWe present a tool for the computation of the differences between two XML music score files and the intuitive visualization of those differences on the two scores side-to-side. Our goal is to implement for music scores a utility similar to the Unix diff command for text files. Its purpose is to identify differences between two score files which are relatively similar (typically two versions of the same file), corresponding to the intuitive notion of difference. The comparison is performed at the granularity level of bars (bars are here the analagous, for scores, of lines of text files), using a Longest Common Subsequence (LCS) algorithm, and, in a second step, at the granularity level of notes or chords (the analoguous of characters in text files). The later operation involves the computation of dedicated edit-distances between strings and trees, based on an abstract model of score content use for disambiguating the content for XML score formats and also decoupling our approach from the exact file format (in general MusicXML or MEI).The visualization is performed using Verovio and works for polyphonic scores and for multiple instruments. Straightforward applications are version control systems and collaborative music score edition, as well as speeding-up tasks that require human supervision, such as the visual inspection of the outcome of optical music recognition (OMR) systems

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

8+8=4: Formalizing Time Units to Handle Symbolic Music Durations

This paper focuses on the nominal durations of musical events (notes and rests) in a symbolic musical score, and on how to conveniently handle these in computer applications. We propose the usage of a temporal unit that is directly related to the graphical symbols in musical scores and pair this with a set of operations that cover typical computations in music applications. We formalize this time unit and the more commonly used approach in a single mathematical framework, as semirings, algebraic structures that enable an abstract description of algorithms/processing pipelines. We then discuss some practical use cases and highlight when our system can improve such pipelines by making them more efficient in terms of data type used and the number of computations.Comment: In Proceedings of the International Symposium on Computer Music Multidisciplinary Research (CMMR 2023), Tokyo, Japa

Evaluating musical score difference: a two-level comparison

International audienc

Évaluation de la correction rythmique des partitions numérisées

National audienceNous proposons une représentation des éléments de gravure musicale ayant trait au rythme – figures de notes, tuplets, ligatures, etc. – sous la forme d’arbres. Cette modélisation permet de lever les ambiguités et redondances contenues dans les différents encodages de partitions numériques, qui sont des sources d’incohérences majeures.Le modèle est développé théoriquement, puis nous présentons son intégration dans des procédures de vérification d’encodages. Il est également montré que l’on peut l’utiliser pour des tâches de génération de partitions

The ACCompanion: Combining Reactivity, Robustness, and Musical Expressivity in an Automatic Piano Accompanist

This paper introduces the ACCompanion, an expressive accompaniment system. Similarly to a musician who accompanies a soloist playing a given musical piece, our system can produce a human-like rendition of the accompaniment part that follows the soloist's choices in terms of tempo, dynamics, and articulation. The ACCompanion works in the symbolic domain, i.e., it needs a musical instrument capable of producing and playing MIDI data, with explicitly encoded onset, offset, and pitch for each played note. We describe the components that go into such a system, from real-time score following and prediction to expressive performance generation and online adaptation to the expressive choices of the human player. Based on our experience with repeated live demonstrations in front of various audiences, we offer an analysis of the challenges of combining these components into a system that is highly reactive and precise, while still a reliable musical partner, robust to possible performance errors and responsive to expressive variations.Comment: In Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI-23), Macao, China. The differences/extensions with the previous version include a technical appendix, added missing links, and minor text updates. 10 pages, 4 figure

Automatic Note-Level Score-to-Performance Alignments in the ASAP Dataset

Several MIR applications require fine-grained note alignments between MIDI performances and their musical scores for training and evaluation. However, large and high-quality datasets with this kind of data are not available, and their manual creation is a very time-consuming task that can only be performed by field experts. In this paper, we evaluate state-of-the-art automatic note alignment models applied to dataset generation. We increase the accuracy and reliability of the produced alignments with models that flexibly leverage existing annotations such as beat or measure alignments. We thoroughly evaluate these segment-constrained models and use the best to create note alignments for the ASAP dataset, a large dataset of solo piano MIDI performances beat-aligned to MusicXML scores. The resulting note alignments are manually checked and publicly available at: https://github.com/CPJKU/asap-dataset. The contributions of this paper are four-fold: (1) we extend the ASAP dataset with reliable note alignments, thus creating (n)ASAP, the largest available fully note-aligned dataset, comprising more than 7 M annotated notes and close to 100 hours of music; (2) we design, evaluate, and publish segment-constrained models for note alignments that flexibly leverage existing annotations and significantly outperform automatic models; (3) we design, evaluate, and publish unconstrained automatic models for note alignment that produce results on par with the state of the art; (4) we introduce Parangonada, a web-interface for visualizing and correcting alignment annotations

Partitura: A Python Package for Symbolic Music Processing

Partitura is a lightweight Python package for handling symbolic musical information. It provides easy access to features commonly used in music information retrieval tasks, like note arrays (lists of timed pitched events) and 2D piano roll matrices, as well as other score elements such as time and key signatures, performance directives, and repeat structures. Partitura can load musical scores (in MEI, MusicXML, Humdrum **kern, and MIDI formats), MIDI performances, and score- to-performance alignments. The package includes some tools for music analysis, such as automatic pitch spelling, key signature identification, and voice separation. Partitura is an open-source project and is available at https://github.com/CPJKU/partitura/