Music Recommendation and Classification Utilizing Machine Learning and Clustering Methods

This paper will discuss the development of a music classification system as a component in a music recommendation system. The front-end portion of the system is an Android Media Player application named SmartPlayer. The player is an intelligent mobile recommendation system as well as a media player that is capable to playing high quality videos. In this paper the specifics of the underlying system and the front-end components will be discussed in detail. Other methods and future aspirations will also be discussed. The system performs automatic genre classification with one feature-vector per audio file. The backbone of the system is utilizing the existing SmartPlayer SQL database containing some 100,000 YouTube music videos in mp4 format. The system utilizes multiple components of the Marsyas (Music Analysis, Retrieval and Synthesis for Audio Signals) open source project, as well as STANN (Simple Thread-safe Approximate Nearest Neighbors). A different method that will be introduced is the K-nearest neighbor method to cluster and compare existing videos in the database. The dynamic time warping method will be used to compare different time series data derived from MFCCs. This method is generally used in comparing two time series data plots but does so with respect to relative aspects of the data.