MusicOWL

Jones, J. J.; Braga, Diego de Siqueira; Tertuliano, Kleber; Kauppinen, Tomi

doi:10.1145/3106426.3110325

Cited by 8 publications

(1 citation statement)

References 4 publications

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“…For example, the MIDI Linked Data Cloud [26] models symbolic music descriptions encoded in MIDI format. The Music Theory Ontology (MTO) [27] aims to describe theoretical concepts related to a music composition, while The Music Score Ontology (Music OWL) [28] represents similar concepts with a focus on music sheet notation. Finally, the Music Notation Ontology [29] focuses on the core "semantic" information present in a score.…”

Section: Modelling Semantics In Music Datamentioning

confidence: 99%

The Harmonic Memory: a Knowledge Graph of harmonic patterns as a trustworthy framework for computational creativity

Berardinis

Meroño‐Peñuela

Poltronieri

et al. 2023

Proceedings of the ACM Web Conference 2023

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Computationally creative systems for music have recently achieved impressive results, fuelled by progress in generative machine learning. However, black-box approaches have raised fundamental concerns for ethics, accountability, explainability, and musical plausibility. To enable trustworthy machine creativity, we introduce the Harmonic Memory, a Knowledge Graph (KG) of harmonic patterns extracted from a large and heterogeneous musical corpus. By leveraging a cognitive model of tonal harmony, chord progressions are segmented into meaningful structures, and patterns emerge from their comparison via harmonic similarity. Akin to a music memory, the KG holds temporal connections between consecutive patterns, as well as salient similarity relationships. After demonstrating the validity of our choices, we provide examples of how this design enables novel pathways for combinational creativity. The memory provides a fully accountable and explainable framework to inspire and support creative professionals -allowing for the discovery of progressions consistent with given criteria, the recomposition of harmonic sections, but also the co-creation of new progressions. CCS CONCEPTS• Applied computing → Sound and music computing; • Computing methodologies → Knowledge representation and reasoning; Ontology engineering.

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Section: Modelling Semantics In Music Datamentioning

confidence: 99%

The Harmonic Memory: a Knowledge Graph of harmonic patterns as a trustworthy framework for computational creativity

Berardinis

Meroño‐Peñuela

Poltronieri

et al. 2023

Proceedings of the ACM Web Conference 2023

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The Polifonia Ontology Network: Building a Semantic Backbone for Musical Heritage

de Berardinis,

Carriero,

Jain

et al. 2023

Lecture Notes in Computer Science

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In the music domain, several ontologies have been proposed to annotate musical data, in both symbolic and audio form, and generate semantically rich Music Knowledge Graphs. However, current models lack interoperability and are insufficient for representing music history and the cultural heritage context in which it was generated; risking the propagation of recency and cultural biases to downstream applications. In this article, we propose the Polifonia Ontology Network (PON) for music cultural heritage, centred around four modules: Music Meta (metadata), Representation (content), Source (provenance) and Instrument (cultural objects). We design PON with a strong accent on cultural stakeholder requirements and competency questions (CQs), contributing an NLP-based toolkit to support knowledge engineers in generating, validating, and analysing them; and a novel, high-quality CQ dataset produced as a result. We show current and future use of these resources by internal project pilots, early adopters in the music industry, and opportunities for the Semantic Web and Music Information Retrieval communities.

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