Symbolic Music Representations for Classification Tasks: A Systematic Evaluation

Music Information Retrieval (MIR) has seen a recent surge in deep learning-based approaches, which often involve encoding symbolic music (i.e., music represented in terms of discrete note events) in an image-like or language like fashion. However, symbolic music is neither an image nor a sentence, and research in the symbolic domain lacks a comprehensive overview of the different available representations. In this paper, we investigate matrix (piano roll), sequence, and graph representations and their corresponding neural architectures, in combination with symbolic scores and performances on three piece-level classification tasks. We also introduce a novel graph representation for symbolic performances and explore the capability of graph representations in global classification tasks. Our systematic evaluation shows advantages and limitations of each input representation. Our results suggest that the graph representation, as the newest and least explored among the three approaches, exhibits promising performance, while being more light-weight in training

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

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/

The Match File Format: Encoding Alignments Between Scores and Performances

This paper presents the specifications of match: a file format that extends a MIDI human performance with note-, beat-, and downbeat-level alignments to a corresponding musical score. This enables advanced analyses of the performance that are relevant for various tasks, such as expressive performance modelling, score following, music transcription, and performer classification. The match file includes a set of score-related descriptors that makes it usable also as a bare-bones score representation. For applications that require the use of structural score elements (e.g., voices, parts, beams, slurs), the match file can be easily combined with the symbolic score. To support the practical application of our work, we release a corrected and upgraded version of the Vienna 4x22 dataset of scores and performances aligned with match files

MUSIC GENRE DESCRIPTOR FOR CLASSIFICATION BASED ON TONNETZ TRAJECTORIES

International audienceDans cet article, nous présentons un nouveau descripteur pour la classification automatique du style musical. Notre méthode consiste à définir une trajectoire harmonique dans un espace géométrique, le Tonnetz, puis à la résumer à ses valeurs de centralité, qui constituent les descripteurs. Ceux-ci, associés à des descripteurs classiques, sont utilisés comme caractéristiques pour la classification. Les résultats montrent des scores F 1 supérieurs à 0,8 avec une méthode classique de forêts aléatoires pour 8 classes (une par compositeur), et supérieurs à 0,9 pour une classification en 4 classes de style ou période de composition

Musical genre descriptor for classification based on Tonnetz trajectories

National audienceDans cet article, nous présentons un nouveau descripteur pour la classification automatique du style musical. Notre méthode consiste à définir une trajectoire harmonique dans un espace géométrique, le Tonnetz, puis à la résumer à ses valeurs de centralité, qui constituent les descripteurs. Ceux-ci, associés à des descripteurs classiques, sont utilisés comme caractéristiques pour la classification. Les résultats montrent des scores F 1 supérieurs à 0,8 avec une méthode classique de forêts aléatoires pour 8 classes (une par compositeur), et supérieurs à 0,9 pour une classification en 4 classes de style ou période de composition