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Andrea Poltronieri

dblp:314/0002 · DBLP profile ↗
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4ranked-venue papers
0as first author
4since 2021 · last 2025
0000-0003-3848-7574ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 3 · 3 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
2 papers
Representation and self-supervised learning · 82% Generative modeling · 18%
Computer graphics and multimedia
1 paper
Audio and music processing · 100%
Databases, data mining, and information retrieval
1 paper
Knowledge graphs · 100%

Topics — the 1 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Generative modeling
computational creativity
0.212023
The Harmonic Memory: a Knowledge Graph of harmonic patterns as a trustworthy framework for computational creativity · WWW 2023

Methods — techniques the papers use, named apart from their topics

self-supervised learning · 1.7multi-feature masked token prediction · 1.7
YearPublicationVenuePosition
2025 OMAR-RQ: Open Music Audio Representation Model Trained with Multi-Feature Masked Token Prediction
Pablo Alonso-Jiménez, Pedro Ramoneda, Recep Oguz Araz, Andrea Poltronieri, Dmitry Bogdanov
ACM Multimedia4
2023 Classifying Sequences by Combining Context-Free Grammars and OWL Ontologies
abstract
This paper describes a pattern to formalise context-free grammars in OWL and its use for sequence classification. The proposed approach is compared to existing methods in terms of computational complexity as well as pragmatic applicability, with examples in the music domain.
Nicolas Lazzari, Andrea Poltronieri, Valentina Presutti
ESWC2
2023 The Polifonia Ontology Network: Building a Semantic Backbone for Musical Heritage
abstract
Abstract 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.
Jacopo de Berardinis, Valentina Anita Carriero, Nitisha Jain, Nicolas Lazzari, Albert Meroño-Peñuela, Andrea Poltronieri, Valentina Presutti
ISWC6
2023 The Harmonic Memory: a Knowledge Graph of harmonic patterns as a trustworthy framework for computational creativity
abstract
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.
Jacopo de Berardinis, Albert Meroño-Peñuela, Andrea Poltronieri, Valentina Presutti
WWW3