The Web and other digital technologies have democratised music creation, reception, and analysis, putting music in the hands, ears, and minds of billions of users. Music digital libraries typically focus on an essential subset of this deluge - commercial and academic publications, and historical materials - but neglect to incorporate contributions by scholars, performers, and enthusiasts, such as annotations or performed interpretations of these artifacts, despite their potential utility for many types of users. In this paper we consider means by which digital libraries for musicology may incorporate such contributions into their collections, adhering to principles of FAIR data management and respecting contributor rights as outlined in the EU's General Data Protection Regulation. We present an overview of centralised and decentralised approaches to this problem, and propose hybrid solutions in which contributions reside in a) user-controlled personal online datastores, b) decentralised file storage, and c) are published and aggregated into digital library collections. We outline the implementation of these ideas using Solid, a Web decentralisation project building on W3C standard technologies to facilitate publication and control over Linked Data. We demonstrate the feasibility of this approach by implementing prototypes supporting two types of contribution: Web Annotations describing or analysing musical elements in score encodings and music recordings; and, music performances and associated metadata supporting performance analyses across many renditions of a given piece. Finally, we situate these ideas within a wider conception of enriched, decentralised, and interconnected online music repositories.

@en

The turn toward the digital has opened up previously difficult to access musical materials to wider musicological scholarship. Digital repositories provide access to publicly licensed score images, score encodings, textual resources, audiovisual recordings, and music metadata. While each repository reveals rich information for scholarly investigation, the unified exploration and analysis of separate digital collections remains a challenge. TROMPA-Towards Richer Online Music Public-domain Archives-addresses this through a knowledge graph interweaving composers, performers, and works described in established digital music libraries, facilitating discovery and combined access of complementary materials across collections. TROMPA provides for contribution of expert insights as citable, provenanced annotations, supporting analytical workflows and scholarly communication. Beyond scholars, the project targets four further user types: instrumental players; choir singers; orchestras; and music enthusiasts; with corresponding web applications providing specialised views of the same underlying knowledge graph. Thus, scholars' annotations provide contextual information to other types of users; while performers' rehearsal recordings and performative annotations, conductors' marked up scores, and enthusiasts' social discussions and listening behaviours, become available to scholarly analysis (per user consent). The knowledge graph is exposed as Linked Data, adhering to the FAIR principles of making data Findable, Accessible, Interoperable, and Re-usable, and supporting further interlinking, re-interpretation and re-use beyond the immediate scope of the project.

@en

This paper introduces the AcousticBrainz Genre Dataset, a large-scale collection of hierarchical multi-label genre annotations from different metadata sources. It allows researchers to explore how the same music pieces are annotated differently by different communities following their own genre taxonomies, and how this could be addressed by genre recognition systems. Genre labels for the dataset are sourced from both expert annotations and crowds, permitting comparisons between strict hierarchies and folksonomies. Music features are available via the Acoustic- Brainz database. To guide research, we suggest a concrete research task and provide a baseline as well as an evaluation method. This task may serve as an example of the development and validation of automatic annotation algorithms on complementary datasets with different taxonomies and coverage. With this dataset, we hope to contribute to developments in content-based music genre recognition as well as cross-disciplinary studies on genre metadata analysis. @en

This paper introduces the AcousticBrainz Genre Dataset, a large-scale collection of hierarchical multi-label genre annotations from different metadata sources. It allows researchers to explore how the same music pieces are annotated differently by different communities following their own genre taxonomies, and how this could be addressed by genre recognition systems. Genre labels for the dataset are sourced from both expert annotations and crowds, permitting comparisons between strict hierarchies and folksonomies. Music features are available via the AcousticBrainz database. To guide research, we suggest a concrete research task and provide a baseline as well as an evaluation method. This task may serve as an example of the development and validation of automatic annotation algorithms on complementary datasets with different taxonomies and coverage. With this dataset, we hope to contribute to developments in content-based music genre recognition as well as cross-disciplinary studies on genre metadata analysis@en

This paper provides an overview of the AcousticBrainz Genre Task organized as part of the MediaEval 2018 Benchmarking Initiative for Multimedia Evaluation. The task is focused on content-based music genre recognition using genre annotations from multiple sources and large-scale music features data available in the AcousticBrainz database. The goal of our task is to explore how the same music pieces can be annotated differently by different communities following different genre taxonomies, and how this should be addressed by content-based genre recognition systems. We present the task challenges, the employed ground-truth information and datasets, and the evaluation methodology.@en

This paper provides an overview of the AcousticBrainz Genre Task organized as part of the MediaEval 2017 Benchmarking Initiative for Multimedia Evaluation. The task is focused on content-based music genre recognition using genre annotations from multiple sources and large-scale music features data available in the AcousticBrainz database. The goal of our task is to explore how the same music pieces can be annotated differently by different communities following different genre taxonomies, and how this should be addressed by content-based genre recognition systems. We present the task challenges, the employed ground-truth information and datasets, and the evaluation methodology. @en