These attributes describe mainly sound characteristics. Popularity - The higher the value the more popular the song is. Release Year - The year each song was released. Acousticness - The higher the value the more acoustic the song is. Valence - The higher the value, the more positive mood for the song. Loudness - The higher the value, the louder the song (in dB). Danceability - The higher the value, the easier it is to dance to this song. Energy - The energy of a song, the higher the value, the more energetic. Spotify provides the following attributes for each song:īeats Per Minute (BPM) - The tempo of the song.
The attributes, or features in machine learning terminology, are the variables that our mathematical models take into consideration when they try to predict stuff. Extracting interesting attributes from songs So, I was quite curious what this number crunching would reveal about my preferences.
My Favorites playlist contains 321 songs covering a wide spectrum of music genres.
However, I’d appreciate any piece of music with narrative lyrics, lush orchestrations and dynamic melody progressions. I would describe my music taste as quite open, but speaking in terms of genres, I am probably into what is called indie rock or art rock, with an occasional influence of folk, chamber and baroque. This tool was built by Echonest, a small startup that Spotify bought two years ago for $100M and has contributed to the success of automated music recommendation, such as the Discover Weekly playlists.īy getting all these interesting song attributes like Danceability, Energy, Acousticness and so on, I got the idea to do a statistical analysis of my favourite songs. As a long-time music fan and early adopter, I use Spotify probably since day-one by religiously saving interesting songs to my Favorites playlist. A couple of months ago I stumbled upon a cute web app that lets you log in with your Spotify account and see some interesting attributes of the songs in your playlists.
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