A machine (a RaspberryPi, a home server, a VPS, an unused tablet, etc.) to run the Platypush automation.The database initialization script has been tested against Postgres, but it should be easy to adapt to MySQL or SQLite with minimal modifications. A database to store your listening history and suggestions.Monitor new releases from the RSS feed, and create a weekly Release Radar playlist containing the items from artists that we have listened to at least once.Generate a discover weekly playlist based on a simple score that ranks suggestions by match score against the tracks listened to on a certain period, and increases the weight of suggestions that occur multiple times.Periodically inspect our newly listened tracks, and use the last.fm API to retrieve similar tracks.Store our listening history to a local database, or synchronize it with a scrobbling service like last.fm.We will use Platypush to handle the following features: Generate a weekly playlist with the new releases by filtering those from artists that you’ve listened to at least once.Get the newly released albums and singles by subscribing to an RSS feed.Generate a Discover Weekly playlist similar to Spotify’s without relying on Spotify.Calculate the suggested tracks that may be similar to the music you’ve recently listened to by using the Last.FM API.Use Platypush to keep track of the music automatically you listen to from any of your devices.And I decided that the time had come for me to generate my service-agnostic music suggestions automatically: it’s not rocket science anymore, there are plenty of services that you can piggyback on to get artists or tracks similar to some music given as input, and there’s just no excuses to feel locked in by Spotify, Google, Tidal or some other cloud music provider. I asked myself why my music discovery experience should be so tightly coupled to one cloud service on earth. Companies like Spotify use such features as a lock-in mechanism - you can check out any time you like, but if you do, nobody else will provide you with their clever suggestions.Īfter migrating from Spotify to Tidal in the past couple of months (TL DR: Spotify f*cked up their developer experience multiple times over the past decade, and their killing off libspotify without providing any alternatives was the last nail in the coffin for me), I felt like missing their smart mixes, discovery and new releases playlists.īut, on the other hand, Tidal took a while to learn my listening habits, and even when it did, it often generated smart playlists that were an inch below Spotify’s. If you cancel your Spotify subscription, you also lose those smart features. ![]() Those features are tightly coupled with the service you use.There’s no transparent way to tell the algorithm, “hey, actually, I’d like you to suggest me more this kind of music - or maybe calculate suggestions only based on the music I’ve listened to in this time range, or maybe weigh this genre more.” In the past months, Spotify would often suggest me tracks from the same artists that I had already listened to or skipped in the past. You don’t know how Spotify figures out which songs should be picked in your smart playlists. The problem is that these services come with heavy trade-offs: ![]() Spotify, Tidal, and other music streaming services offer you features such as Discovery Weekly or Release Radar playlists, respectively, filled with tracks that you may like or newly released tracks that you may be interested in. There is a feature that I haven’t yet covered in my previous articles: the automation of your music collection. I have already written an article on how to leverage mopidy (with its tons of integrations, including Spotify, Tidal, YouTube, Bandcamp, Plex, TuneIn, SoundCloud, etc.), Snapcast (with its multi-room listening experience out of the box), and Platypush (with its automation hooks that allow you to create if-this-then-that rules for your music events easily) to take your listening experience to the next level while using open protocols and easily extensible open-source software. I have been an enthusiastic user of mpd and mopidy for nearly two decades.
0 Comments
Leave a Reply. |