From discord to harmony: decomposed consonance-based training for improved audio chord estimation

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• dc.contributor.author Poltronieri, Andrea
• dc.contributor.author Serra, Xavier
• dc.contributor.author Rocamora, Martín
• dc.date.accessioned 2025-09-05T06:26:24Z
• dc.date.available 2025-09-05T06:26:24Z
• dc.date.issued 2025
• dc.description Comunicació presentada al 26th International Society for Music Information Retrieval Conference (ISMIR 2025), celebrada a Daejeon (Korea) del 21 al 25 de setembre del 2025
• dc.description.abstract Audio Chord Estimation (ACE) holds a pivotal role in music information research, having garnered attention for over two decades due to its relevance for music transcription and analysis. Despite notable advancements, challenges persist in the task, particularly concerning unique characteristics of harmonic content, which have resulted in existing systems' performances reaching a glass ceiling. These challenges include annotator subjectivity, where varying interpretations among annotators lead to inconsistencies, and class imbalance within chord datasets, where certain chord classes are over-represented compared to others, posing difficulties in model training and evaluation. As a first contribution, this paper presents an evaluation of inter-annotator agreement in chord annotations, using metrics that extend beyond traditional binary measures. In addition, we propose a consonance-informed distance metric that reflects the perceptual similarity between harmonic annotations. Our analysis suggests that consonance-based distance metrics more effectively capture musically meaningful agreement between annotations. Expanding on these findings, we introduce a novel ACE conformer-based model that integrates consonance concepts into the model through consonance-based label smoothing. The proposed model also addresses class imbalance by separately estimating root, bass, and all note activations, enabling the reconstruction of chord labels from decomposed outputs.
• dc.description.sponsorship This work is supported by IA y Música: Cátedra en Inteligencia Artificial y Música (TSI-100929-2023-1), funded by the Secretaría de Estado de Digitalización e Inteligencia Artificial, and the European Union-Next Generation EU, under the program Cátedras ENIA 2022 para la creación de cátedras universidad-empresa en IA, and IMPA: Multimodal AI for Audio Processing (PID2023- 152250OB-I00), funded by the Ministry of Science, Innovation and Universities of the Spanish Government, the Agencia Estatal de Investigación (AEI) and co-financed by the European Union.
• dc.format.mimetype application/pdf
• dc.identifier.citation Poltronieri A, Serra X, Rocamora M. From discord to harmony: decomposed consonance-based training for improved audio chord estimation. Paper presented at: 26th International Society for Music Information Retrieval Conference (ISMIR 2025); 2025 Sep 21-25; Daejeon, Korea. 9p.
• dc.identifier.uri http://hdl.handle.net/10230/71120
• dc.language.iso eng
• dc.publisher International Society for Music Information Retrieval (ISMIR)
• dc.relation.projectID info:eu-repo/grantAgreement/ES/3PE/PID2023-152250OB-I00
• dc.rights Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Attribution: Andrea Poltronieri, Xavier Serra, Martín Rocamora, “From Discord to Harmony: Decomposed Consonance-based Training for Improved Audio Chord Estimation”, in Proc. of the 26th Int. Society for Music Informat. Retrieval Conf., Daejeon, South Korea, 2025.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Decomposed consonance-based training
• dc.subject.keyword Audio chord estimation
• dc.title From discord to harmony: decomposed consonance-based training for improved audio chord estimation
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/publishedVersion