• Caesium@lemmy.worldEnglish
    1·
    3 小时前

    I’ve been finding new music as last year I discovered a group that shares 50 songs a year, that’s been running since 2009. I actually already knew about it as artists I already follow are involved with the group, I just somehow never connected the dots until recently. This shit is gonna last me ages

  • froufox@lemmy.blahaj.zoneEnglish
    1·
    4 小时前

    My bandcamp findings dump this year so far :3

  • 58008@lemmy.worldEnglish
    8·
    17 小时前

    Wikipedia rabbitholing is my preferred method. Start on an article about a band or genre you like, then just glance through for influences and subgenres etc., read those articles to find new band names, give 'em a quick listen on whatever platform you use (usually just YouTube for me), and continue the process as needed i.e. if you don’t like what you hear for a given artist, just keep clicking till you find the next one. It sounds like it’d take forever this way, but I’ve found new bands I love within about 10 minutes of clicking, and this is consistently the case. Algorithms have never, ever recommended me anything I actually liked, and this is true for music, games, TV shows and whatever else. They just don’t work on any meaningful level.

  • KoboldCoterie@pawb.socialEnglish
    64·
    1 天前

    Music discovery algorithms don’t have to be AI slop. Some of them used to work effectively peer to peer based on likes.

    You like songs, as does everyone else. The algorithm compares the songs you liked to what other people liked, finds people who liked a high percentage of the things you did, and recommends you other songs that they liked, and vice versa. Basically “Many people who liked [song you like] also liked [song you maybe haven’t heard]”.

    • strawberry_enjoyer42@lemmy.blahaj.zoneOPEnglish
      13·
      24 小时前

      I said “via algorithms and AI slop”, two seperate ways of finding music, “AI slop” meaning Spotify-style playlist nonsense.

      Also, algorithms create feedback loops, where popular things get recommended more, even among specific niches.

      • john_lemmy@slrpnk.netEnglish
        2·
        9 小时前

        True. But even that can be tackled in recommendation algorithms (or attempted). The main issue I see is that the companies that produce them don’t have their goals aligned with yours and rerank results to benefit their bottom line. That said, I’ve gotten much better recommendations for books, music and games from people online and friends than from any such system. Worst case the recommendation is not great and that is still an opportunity to talk to the person who recommended it.

    • NightFantom@slrpnk.netEnglish
      13·
      1 天前

      Sadly once you like one song that’s been on the radio once, it starts spiralling into other songs (often good even) you know from the radio and 0 other songs. With things that kind of come in sets (like “songs that played often on X channel in the 90s”) it becomes quickly a game of complete the set rather than discovering new music you’d also like.

      • 8uurg@lemmy.worldEnglish
        1·
        2 小时前

        There collaborative filtering algorithms do tend to have a popularity bias. The other downside is that these algorithms also don’t help new music and musicians get found.

        • NightFantom@slrpnk.netEnglish
          2·
          15 分钟前

          Yes, though I’d argue that’s the same downside :D

          I’ve worked in recommender systems for news specifically myself for a couple years so if anyone has some questions that aren’t too identifiable, AMA I guess

          We had a system that combined your reading history (from a tracking pipeline that already existed similar to google analytics) (though it could have just as easily been sent from the front-end with the request for recommendations as we didn’t precompute anything) with several scoring systems (from simple things like popularity score per article which ignores your history, to multiple complex pretrained models that use your history to calculate a score optimising for some variable), all of which can be weighed and then the scores are added/multiplied and sorted and bam, out rolls your personal list of recommendations.

          We could easily tone down (even turn negative) the populatity bias, but it turned out that it was just a strong predictor for what people wanted to read (measured in both click through rate and dwell time on the clicked page), so we’re not entirely sure whether news is just different (if you spent a couple of minutes per day scrolling past headlines you’ll have seen everything from today, and clicked what you cared about, and left again) or we weren’t really catering to the crowd that would be helped by getting non-popular recommendations, because they’re drowned out by the crowd that’s just looking for whatever’s popular.

          Anyway, AMA

    • doleo@lemmy.oneEnglish
      31·
      1 天前

      With respect, that’s a bit like saying twitter doesn’t have to be a far right hate speech enabler. What something is, and what something could be, I’m afraid in this case are irreconcilable.

  • belluck@lemmy.blahaj.zoneEnglish
    4·
    17 小时前

    Recent metal album I picked up on Bandcamp Friday: Cairiss - Wilderness

    I do occasionally hear metal but this is the first of this subgenre I‘ve listened to. Really enjoyed it though, so I‘m open to recommendations

  • rindo25@lemmy.worldEnglish
    3·
    17 小时前

    I recommend these 3 bands:

    Mr. Bungle Grim Salvo Viagra Boys

    Word to the wise every Mr. Bungle album is different so start with California for something easy and stable.

  • ButteryMonkey@piefed.socialEnglish
    2·
    15 小时前

    I do this for cartoons. If you tell me about a cartoon I haven’t heard of my immediate reaction is to download it.

    I have almost 600 cartoon series, none older than late 80s.

  • Skankboot@sh.itjust.worksEnglish
    13·
    24 小时前

    Youtube’s algo has been pretty good at giving me artist/song recommendations… admittwdly after over a decade of liking and subscribing to a ton of bands on the platform. Brought me IDLES and Viagra Boys first LPs before anyone else, and just this week turned me on to Vancouver band PISS. They’re fucking raw, take heed of the lead’s warning before they start.

    https://youtu.be/_v0iGBBiXBI

    • Baŝto@discuss.tchncs.deEnglish
      2·
      9 小时前

      Same. Over the last decade that was one of my main main ways of finding new music. Don’t really had friends with similar tastes and record labels luckily all had official accounts. Plus promotion accounts who upload music from certain genres. There are definitely genres I found via the algorithm.

  • crank0271@lemmy.worldEnglish
    13·
    1 天前

    Don’t forget music blogs and reviewers (which may technically fall under “randos”). In this age of bland algorithmic recommendations, going back to reading articles and reviews written by someone with similar musical tastes has once again become my favorite way to discover new music.

      • crank0271@lemmy.worldEnglish
        3·
        18 小时前

        You may have better luck finding websites that are associated with genres of music that you enjoy, but there’s always Pitchfork. I’ve also appreciated recommendations such as this from Terrence O’Brien over at The Verge.