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Add Octave to Music Players list. #46

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Expand Up @@ -194,6 +194,7 @@ Overall, I strongly advise against closed source and ads -- not just because of
- **Tinder for Songs**. Plays the most commented 10 seconds of a song on SoundCloud and adds it to a playlist if you like it. Inspired by Soundsieve (https://github.com/wilzh40/SoundSieve) which is unfortunately dead, and fab.fm which has a different song discovery method.
- Note: This already exists for Spotify, namely [Tuneful](https://www.tuneful.app/) for a webapp, [Discz](https://www.reddit.com/r/spotify/comments/ojogbt/i_built_a_tinder_for_spotify_so_you_can_make/) which has an iOS app, and [Swipify](https://www.reddit.com/r/spotify/comments/mov9rq/swipify_a_tinderstyle_android_music_discovery_app/) on the Play Store!
- DONE: By [Aditya Saravana](https://github.com/adityasaravana/Tuneder) for Apple Music on iOS
- [Octave](https://github.com/mohsen7778/octave-music-player) - Free, serverless Spotify alternative built with vanilla JS.
- DONE: By [Kevin Grosso](https://github.com/k0dev/sc-explorer) for Soundcloud as a Webapp
- **Responsive AI DJ**: Uses a camera to monitor the movement of the people in the crowd and run sentiment analysis on their faces. Notices how that changes as it changes the music, with higher weights for more people. Can then "gradient descent" towards the optimal AI generated music for that crowd, keeping in mind things like repetitiveness and how the audience composition changes over time. Want to add variety over just beats, so give it some samples and voices to throw in as well.
- **Taste Discoverer**: The most unique and best music is often hard to surface via standard recommendation algorithms, because standard algorithms like collaborative filtering/matrix decomposition are unable to recommend niche songs. A more interesting algorithm is to find all of the artists that you like most/listen to most, and take *their* favorite songs, then take those artists' favorite songs, and so on. This lets you get more niche while feeling more connected to each artist, and as long as you can filter on each step, you can keep the taste exploration aligned with your own even in a complete absense of data. There's plenty of such playlists on Spotify and Soundcloud.
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