Automatic song genre classification using visual features from the spectrogram

Abstract

Due to the advent of the world wide web and hypermedia support, data is being generated and consumed by users at significantly high rates. Among the many types of data is audio data – music in particular – and music files can come with various pieces of information associated with them, such, as name, author, album, and genre. These are called ID3 tags, and for a long time, the pursuit towards automation of the creation of these ID3 tags has been under way. The goal of this project is to focus on one of the aforementioned ID3 tags: the genre. Simply put, a genre is a label used to classify songs under pre-determined categories, and the system will aspire to automatically classify a given song under a genre. The area of genre classification has largely been focused on using the audio domain to carry out the analysis and feature vectors required. While this may seem at first like the logical approach, most research in the area has hit a glass ceiling where improvement on existing results is difficult. Clearly, more research into a newer approach would revitalise the area and work towards improving on such results. Hence, the approach that this paper will take makes use of image processing, or more precisely, visually analysing the spectrogram (time vs frequency graph), which is a more unique approach towards genre classification.