Some years ago, the iPod had a very cool feature called Genius, which created a playlist on-the-fly based on a few example songs. I thought that artificial intelligence could be applied to this "music intuition" as a music recommendation system based on simply listening to a song (and, of course, having an encyclopaedic knowledge of music). Or, if a song is playing, one that would go well with it usually comes to mind immediately. I can almost instantly tell whether I am going to like a song or not just by listening to it for a few seconds. I have collected many rare records over the years and done a bit of deejaying on the radio and in clubs. In my book, it's not about how you play, but what you play. To be honest, I have always found that kind of DJ rather boring: the better they are technically, the more it sounds just like one interminable song. There are a number of automatic DJ tools around, which cleverly match the tempo of one song with another and mix the beats. The code for the website is available here. UPDATE: Check out the results after applying to 320,000 tracks on Spotify here. UPDATE: You can now use this model even more easily than before in the Hugging Face hub. The fact that people have been consistently continuing to use it is testament to how well it works, but it was about time I included tracks released since 2018! I have added a train directory to this repo where you can find a README with detailed instructions on how to obtain datasets and train your own model from scratch. UPDATE: After nearly 5 years, I have finally got around to re-training the model deployed at with a million playlists and a million tracks. Robert Dargavel Smith - Advanced Machine Learning end of Masters project ( MBIT School, Madrid, Spain)
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