A Novel Approach to Music Genre Classification using Natural Language Processing and Spark

Abstract

With the advent of digitized music, many online streaming companies such as Spotify have capitalized on a listener's need for a common stream platform. An essential component of such a platform is the recommender systems that suggest the constituent user base, related tracks, albums and artists. In order to sustain such a recommender system, labeling data, to indicate which genre it belongs to is essential. Most recent academic publications that deal with music genre classification focus on the use of deep neural networks developed and applied within the music genre classification domain. This paper attempts to use some of the highly sophisticated techniques, such as Hierarchical Attention Networks that exist within the text classification domain in order to classify tracks of different genres. In order to do this, the music is first separated into different tracks (drums, vocals, bass and accompaniment) and converted into symbolic text data. Due to the sophistication of the distributed machine learning system present in this paper, it is capable of classifying contemporary genres with impressive accuracy, when comparing the results with that of competing classifiers. It is also argued that through the use text classification, the expert knowledge which musicians and people involved with musicological techniques, can be attracted to improving recommender systems within the music information retrieval research domain.