Measure listener biometrics against song timestamps. Wonder if you could get any decent correlations around it using basic biometrics available in smart watches and fitness devices.
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I think HR is way too coarse a mechanism. Can get 90% of the value by noticing the slices that cause people to bring Spotify/iTunes into the foreground ("whoa what's that song" effect).
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Yeah you're right. Same with skipping and the point in time a person decides to actually like a track I guess. Same old desire to find a non-invasive way to measure brain activity :)
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Soundcloud might have a basic version of this data. Users comment on timestamps of tracks.
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Soundcloud comments? Like they're spread out to specific moments of the song so maybe you can infer positive comments and use that?
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It's unfortunate that most of the comments seem to be spam/pubbing other SoundCloud accounts
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Bigger problem might be how to actually get the comment data set! You can probably scrub some of it where there are links included etc.
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Soundcloud most likely has that data already
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The impediment to good song recs is feature extraction from audio. Once ML can auto transcribe songs to sheet music, identify instruments, lyrical themes, no reason that this cant be solved.
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Shazam has this data already and does interesting things with it. Users use the app during or right after the part of the song that catches their attention.
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This would work even better for podcasts than for music, since you can apply NLP to the clip.
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I think it could be the same. There’s plenty of computable data in a clip of music
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Damn, wish I could get recommendations based on this. Disliking slices would be great too
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