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The AcousticBrainz Genre Dataset: Multi-Source, Multi-Level, Multi-Label, and Large-Scale.

, , , , и . ISMIR, стр. 360-367. (2019)

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Enriched Music Representations With Multiple Cross-Modal Contrastive Learning., , , , и . IEEE Signal Process. Lett., (2021)Music Recommender Systems., , , , и . Recommender Systems Handbook, Springer, (2015)From music similarity to music recommendation : computational approaches based on audio features and metadata.. Pompeu Fabra University, Spain, (2013)Music retagging using label propagation and robust principal component analysis., , , и . WWW (Companion Volume), стр. 869-876. ACM, (2012)A content-based system for music recommendation and visualization of user preferences working on semantic notions., , , , , и . CBMI, стр. 249-252. IEEE, (2011)Efficient Supervised Training of Audio Transformers for Music Representation Learning., , и . ISMIR, стр. 824-831. (2023)MusAV: A dataset of relative arousal-valence annotations for validation of audio models., , , и . ISMIR, стр. 650-658. (2022)Music Representation Learning Based on Editorial Metadata from Discogs., , и . ISMIR, стр. 825-833. (2022)Mining metadata from the web for AcousticBrainz., , и . DLfm, стр. 53-56. ACM, (2016)MediaEval 2021: Emotion and Theme Recognition in Music Using Jamendo., , и . MediaEval, том 3181 из CEUR Workshop Proceedings, CEUR-WS.org, (2021)