[Week #1 — Rock or Not? ♫]
☞ This sure does.
We are Defne Tunçer & Kutay Barçin and this is our first article of series of our Machine Learning Course Project about Music Genre Classification, we are going to post our progress till the end of our term periodically. So let’s give it a start!
INTRODUCTION
When there is people, there is music. In the history of music, it has found different forms.
Some rocked the world:
Some brought peace:
Some took us to a galaxy far, far away…
In today’s world, technology has changed the way music reaches us. During ’50s Jazz was the most popular, while ’70s rock’n roll ruled the world. Nowadays, music is evolved into hundreds of genres — and is just one click away.
Currently, one of the lead music streaming platform Spotify with 180 million active users, has used a complex algorithm to analyze and categorize upwards of 60 million songs on a molecular level — and the micro-classifications now number 1,387 sub-genres in total.
On every Monday Spotify recommends a playlist for every user according to their tastes — which makes Mondays less sufferable.
The dramatic increase in the size of music collections created two challenges: the need to automatically classify a collection, and the need to automatically recommend new songs to a user knowing his/her listening habits. An underlying task in both those challenges is to be able to group songs in semantic categories. In this work we are to classify music genres given input features from music tracks using machine learning techniques.
