Partition based feature processing for improved music classification

Abstract

Identifying desired music amongst the vast amount of tracks in today's music collections has become a task of increasing attention for consumers. Music classification based on perceptual features promises to help sorting a collection according to personal music categories determined by the user's personal taste and listening habits. Regarding limits of processing power and storage space available in real (e.g. mobile) devices necessitates to reduce the amount of feature data used by such classification. This paper compares several methods for feature pruning- experiments on realistic track collections show that an approach attempting to identify relevant song partitions not only allows to reduce the amount of processed feature data by 90% but also helps to improve classification accuracy. They indicate that a combination of structural information and temporal continuity processing of partition based classification helps to substantially improve overall performance. © 2012 Springer-Verlag Berlin Heidelberg.