Music Outlier Detection Using Multiple Sequence Alignment and Independent Ensembles

The automated retrieval of related music documents, such as cover songs or folk melodies belonging to the same tune, has been an important task in the field of Music Information Retrieval (MIR). Yet outlier detection, the process of identifying those documents that deviate significantly from the norm, has remained a rather unexplored topic. Pairwise comparison of music sequences (e.g. chord transcriptions, melodies), from which outlier detection can potentially emerge, has been always in the center of MIR research but the connection has remained uninvestigated. In this paper we firstly argue that for the analysis of musical collections of sequential data, outlier detection can benefit immensely from the advantages of Multiple Sequence Alignment (MSA). We show that certain MSA-based similarity methods can better separate inliers and outliers than the typical similarity based on pairwise comparisons. Secondly, aiming towards an unsupervised outlier detection method that is data-driven and robust enough to be generalizable across different music datasets, we show that ensemble approaches using an entropy-based diversity measure can outperform supervised alternatives.