2026-04-17T18:52:36Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/419852025-12-26T11:26:50Zcom_2072_6col_2072_452952

00925njm 22002777a 4500 dc Bogdanov, Dmitry author Porter, Alastair author Schreiber, Hendrik author Urbano, Julián author Oramas, Sergio author 2019-07-11T08:27:58Z 2019-07-11T08:27:58Z 2019 Comunicació presentada a: 20th International Society for Music Information Retrieval Conference celebrat del 4 al 8 de novembre de 2019 a Delft, Països Baixos. 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. This research has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements No 688382 (AudioCommons) and 770376- 2 (TROMPA), as well as the Ministry of Economy and Competitiveness of the Spanish Government (Reference: TIN2015-69935-P). The AcousticBrainz genre dataset: Multi-source, multi-level, multi-label, and large-scale