Music is like a mirror, and it tells people a lot about who you are and what you care about, whether you like it or not. We love to say “you are what you stream,”:Spotify
Spotify, with a net worth of $26 billion is reigning the music streaming platform today. It currently has millions of songs in its database and claims to have the right music score for everyone. Spotify’s Discover Weekly service has become a hit with the millennials. Needless to say, Spotify has invested a lot in research to improve the way users find and listen to music. Machine Learning is at the core of their research. From NLP to Collaborative filtering to Deep Learning, Spotify uses them all. Songs are analyzed based on their digital signatures for some factors, including tempo, acoustics, energy, danceability etc. to answer that impossible old first-date query: What kind of music are you into?
Companies nowadays use music classification, either to be able to place recommendations to their customers (such as Spotify, Soundcloud) or simply as a product (for example Shazam). Determining music genres is the first step in that direction. Machine Learning techniques have proved to be quite successful in extracting trends and patterns from the large pool of data. The same principles are applied in Music Analysis also.
This is an excerpt taken from a tutorial I wrote on datacamp with the same name.Please find the link to the tutorial here