Music Outlier Detection Using Multiple Sequence Alignment and Independent Ensembles

D. Bountouridis*, Hendrik Vincent Koops, F. Wiering, R.C. Veltkamp

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


    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.
    Original languageEnglish
    Title of host publicationSimilarity Search and Applications
    Subtitle of host publication9th International Conference, SISAP 2016, Tokyo, Japan, October 24-26, 2016, Proceedings
    EditorsLaurent Amsaleg, Michael E. Houle, Erich Schubert
    Place of PublicationCham
    Number of pages15
    ISBN (Electronic)978-3-319-46759-7
    ISBN (Print)978-3-319-46758-0
    Publication statusPublished - 27 Sept 2016

    Publication series

    NameLecture Notes in Computer Science
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


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