They are amazing treasure troves of knowledge waiting to be used. And not just Wikidata, but also projects like MusicBrainz and AcousticBrainz.
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How does working with material under a free license work for you? How can both sides benefit from each other?įirst of all I’m extremely grateful for all the free data out there. Free knowledge and commercial products - it’s not a contradiction, but not what many people may have in mind. (see ) The data exists, it’s just not reachable from Wikidata yet. all Beatles songs are completely annotated with key, chords, tempo, etc. I would love to see more semantic data about music. Are you happy with the API, the data we provide, the documentation? Do you have a request for a feature or an improvement we could work on? Wikidata’s clear structure was very appealing to me.
In my evaluation I used both Wikidata and DBpedia (see ). I was trying to learn genre relationship graphs from another database and needed an “objective” reference graph. And genre relationships are what helped me discover Wikidata. Because of the subgenre relationship between Wikidata genres, it is easy to reason that Hard Rock is a subgenre of Rock, but Calypso is not. Last but not least: beaTunes uses Wikidata to answer questions about genre relationships. For the final ranking of artists found this way, beaTunes uses a simple machine learning approach. Additional information like band membership, influencers etc. It can use Wikidata to look up similar artists, by searching for artists from a region, producing music in a certain genre and having been active during a particular time. The fact that information on Wikidata is typed is very helpful here and helps with disambiguation.īesides using plain textual data from the Wiki universe, beaTunes also exploits relationships.
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Because Wikidata makes it so easy to access DBpedia or Wikipedia data, it is also used when looking up additional a information on albums, TV shows or movies-beaTunes simply displays the first couple of sentences of the corresponding Wikipedia article. The fact that Wikidata is searchable via MusicBrainz and Discogs ids makes this very straight forward and easy. Wikidata is one of the reference sources.
beaTunes essentially acts like a smart spellchecker specialized on music metadata. When a user manually edits song metadata, alternative spellings and additional data is fetched from different sources. What kind of problems does Wikidata solve for you? How did you discover Wikidata? And yet another group likes to work out to music and wants to find tracks that match their running or cycling pace. But some really just want to fix their metadata, others need a good key and tempo detection for their next DJ gig, because they use beatmatching and harmonic mixing. This means that beaTunes appeals to a quite diverse group of users. And that’s essentially the third main functionality: using comprehensive metadata to create great playlists. They come in handy when you want to build playlists of similar sounding songs. These are properties that beaTunes can extract straight from the audio signal. Other important musical features are tonal key, tempo (BPM), timbre, loudness, etc. But textual metadata is only half the story. That database is powered by a mixture of user submissions and data from third parties like MusicBrainz and Discogs (both MusicBrainz and Discogs ids can be found in Wikidata). It can help fix textual metadata (artist, album, title, etc.) using consistency checks and a reference database. Over the years, beaTunes became more than just a hobby and is now my main occupation.
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Back then I still believed Steve Job’s famous promise that the Mac will be the best Java platform ever. The first version was in English only and would run exclusively on OS X. So the idea of a smart music library management and audio analysis tool was born. At the time I had spent multiple years working as a contractor and felt a little burned out from the daily grind. I started coding beaTunes more than ten years ago as a just-for-fun-project. What is beaTunes? How does it work and what problems does it solve? I live and work in Cologne and my main interest is Music Information Retrieval (MIR).
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I’m an independent software developer with almost 20 years of professional experience.
Hendrik Schreiber Foto: Pascal Nordmann CC BY-SA 4.0
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