/Content-based-Music-Recommendation

Project seeking to bridge the semantic gap between music and predicting similar songs

Primary LanguagePythonApache License 2.0Apache-2.0

Content-based-Music-Recommendation System using Deep Learning

Project seeking to bridge the semantic gap between music audio and user preferences

Abstract

The automated curation of music playlists has become a signifcant problem in the last decade, with the colossal rise of streaming platforms e.g. Spotify.

Current state-of-the-art recommender systems depend on the collaborative filtering model. Nevertheless, these systems experience the cold start problem; they break down when no historical data is available and as a result they cannot recommend new and unpopular songs.

In this project, the author proposes a new recommendation model that uses music audio and deep learning to produce playlists based on a given query song.

details explained here: https://medium.com/@silvercloud438/how-i-taught-a-neural-network-to-understand-similarities-in-music-audio-d4fca54c1aed

Code

to use this system:

  • download a dataset (FMA,GTZAN)

  • feature convert data to mel-spectrograms using featureConverter script

  • change file path in model script to feed data and train CNN

  • keep an ordered list of songTitles in text file for library of songs

  • run CNN in predict script

  • run playlist generator

  • output playlist :)

have fun!