Mixonomer is a __Spotify__ playlist manager for creating _smart playlists_. Create mixes with other __Spotify__ playlists and include AI recommendations, playlists are updated multiple times a day. __Last.fm__ integration provides data for listening statistics and visualisation.
I use Mixonomer to create mixes and big genre playlists out of my smaller sub-genre playlists.
Selector is a __Spotify__ listening agent for monitoring what you're listening to and presenting a dashboard of stats. Display __Spotify__ stats that you can't typically see in the apps such as the song descriptor, tempo, key and popularity. Connect to Last.fm to present graphs of how frequently you listen to the current track/album/artist.
I wanted to explore what insights could be found in my 3 years of __Last.fm__ scrobbles when augmented with __Spotify__ data. Ideally, I also wanted to be able to apply the intelligence to the __Mixonomer__ playlist pipeline.
__Spotify__ provides audio features for the tracks on its platform. These features describe a number of qualities for the tracks including how much energy it has and how vocal it is. I investigated whether the set of audio features for my larger genre playlists could be used to classify tracks by genre.
[Read More](/posts/listening-analysis)
[Have a Look](https://github.com/Sarsoo/listening-analysis)
