AI based Music Recommendation system using Deep Learning Algorithms

Abstract

The customized recommendation framework for music should accurately represent private tastes. To obtain tailored feedback for the needs of various viewers, it takes adjustments. To find the better deep learning model for the recommendation may pave a way for a better recommender. Compared to the previous era, with commercial music streaming sites that can be downloaded from mobile devices, digital music availability is currently plentiful. It takes a very long time to figure out all this digital music and induces data exhaustion. It may be helpful to create a music recommendation system that can automatically scan the music libraries and suggest appropriate songs to users. The music provider will anticipate and then give their customers the appropriate songs based on the characteristics of the music previously heard by using the music recommendation system. Our study would like to build a framework for music recommendations that can provide recommendations based on the similarity of audio signal features. This research uses the Convolutional Neural Network (CNN) and the Recurrent Neural Network (RNN). Customized recommendation system for music should effectively represent private preferences. To attain tailored recommendations for the demands of different listeners, it needs changes and therefore, attempting to find a better deep learning model for the recommendation will pave a way for a better recommender.