Editing MusicBrainz (section)

==<span class="anchor" id="ListenBrainz"></span>ListenBrainz==
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[[File:ListenBrainz Logo (2020).svg|thumb|170x170px|Logo of ListenBrainz]]
'''ListenBrainz''' is a [[free and open source]] project that aims to [[Crowdsourcing|crowdsource]] listening data related to digital music and release it under an [[open license]].<ref>{{cite web |title=ListenBrainz Goals |url=https://listenbrainz.org/goals |website=ListenBrainz |access-date=13 February 2021}}</ref> It is a MetaBrainz Foundation project tied to MusicBrainz. It aims to re-implement Last.fm features that were lost following that platform's acquisition by CBS.<ref>{{cite web |last1=O'Brien |first1=Danny |title=Organizing in the Public Interest: MusicBrainz |url=https://www.eff.org/deeplinks/2021/06/organizing-public-interest-musicbrainz |website=Electronic Frontier Foundation |access-date=9 December 2023 |date=3 June 2021}}</ref><ref>{{cite journal |last1=Vigliensoni |first1=Gabriel |last2=Fujinaga |first2=Ichiro |title=The Music Listening Histories Dataset |journal=Proceedings of the 18th International Society for Music Information Retrieval Conference |date=23 October 2017 |pages=96–102 |doi=10.5281/zenodo.1417499 |url=https://zenodo.org/records/1417499 |access-date=17 February 2024 |publisher=ISMIR |location=Suzhou, China}}</ref>

ListenBrainz takes submissions from various media players and services such as [[Music Player Daemon]], [[Spotify]], and [[Rhythmbox]] in the form of listens. ListenBrainz can also import [[Last.fm]] and [[Libre.fm]] scrobbles in order to build listening history. As listens are released under an open license, ListenBrainz is useful for music research in industry and development research.<ref>{{cite book |last1=Singh |first1=Param |last2=Kamlesh |first2=Dutta |last3=Kaye |first3=Robert |last4=Garg |first4=Suyash |title=Proceedings of ICETIT 2019 |chapter=Music Listening History Dataset Curation and Distributed Music Recommendation Engines Using Collaborative Filtering |series=Lecture Notes in Electrical Engineering |chapter-url=https://link.springer.com/chapter/10.1007/978-3-030-30577-2_55 |year=2020 |volume=605 |pages=623–632 |doi=10.1007/978-3-030-30577-2_55 |isbn=978-3-030-30576-5 |s2cid=204103568 |access-date=13 February 2021}}</ref><ref>{{cite book |last1=Yadav |first1=Naina |last2=Singh |first2=Anil |title=Forum for Information Retrieval Evaluation |chapter=Bi-directional Encoder Representation of Transformer model for Sequential Music Recommender System |date=December 2020 |pages=49–53 |doi=10.1145/3441501.3441503 |isbn=9781450389785 |s2cid=231628582 |url=https://dl.acm.org/doi/abs/10.1145/3441501.3441503 |access-date=13 February 2021}}</ref><ref>{{cite book |last1=Schedl |first1=Markus |last2=Knees |first2=Peter |last3=McFee |first3=Brian |last4=Bogdanov |first4=Dmitry |chapter=Music Recommendation Systems: Techniques, Use Cases, and Challenges |title=Recommender Systems Handbook |date=22 November 2021 |pages=927–971 |doi=10.1007/978-1-0716-2197-4_24 |isbn=978-1-0716-2196-7 |chapter-url=https://link.springer.com/chapter/10.1007/978-1-0716-2197-4_24 |access-date=9 December 2023}}</ref><ref>{{cite journal |last1=Pocaro |first1=Lorenzo |last2=Gómez |first2=Emilia |last3=Castillo |first3=Carlos |title=Assessing the Impact of Music Recommendation Diversity on Listeners: A Longitudinal Study |journal=ACM Transactions on Recommender Systems |date=12 July 2023 |volume=2 |pages=1–47 |doi=10.1145/3608487 |url=https://dl.acm.org/doi/10.1145/3608487 |access-date=17 February 2024|arxiv=2212.00592 |s2cid=254125611 }}</ref>