@InProceedings{    bountouridis.ea2016-music,
    author       = {Bountouridis, Dimitrios and Koops, Hendrik Vincent and
                   Wiering, Frans and Veltkamp, Remco C.},
    year         = {2016},
    title        = {Music Outlier Detection Using Multiple Sequence Alignment
                   and Independent Ensembles},
    abstract     = {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.},
    address      = {Tokyo},
    booktitle    = {Proc. of 9th International Conference, SISAP 2016},
    doi          = {10.1007/978-3-319-46759-7_22},
    editor       = {Amsaleg, Laurent and Houle, Michael E. and Schubert,
                   Erich},
    isbn         = {9783319467597},
    keywords     = {computational musicology},
    mendeley-tags= {computational musicology},
    pages        = {286--300},
    publisher    = {Springer Berlin Heidelberg},
    url          = {http://link.springer.com/10.1007/978-3-319-46759-7_22}
}

{"_id":"WttxytMNjwWobcCtS","bibbaseid":"bountouridis-koops-wiering-veltkamp-musicoutlierdetectionusingmultiplesequencealignmentandindependentensembles-2016","authorIDs":[],"author_short":["Bountouridis, D.","Koops, H. V.","Wiering, F.","Veltkamp, R. C."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"propositions":[],"lastnames":["Bountouridis"],"firstnames":["Dimitrios"],"suffixes":[]},{"propositions":[],"lastnames":["Koops"],"firstnames":["Hendrik","Vincent"],"suffixes":[]},{"propositions":[],"lastnames":["Wiering"],"firstnames":["Frans"],"suffixes":[]},{"propositions":[],"lastnames":["Veltkamp"],"firstnames":["Remco","C."],"suffixes":[]}],"year":"2016","title":"Music Outlier Detection Using Multiple Sequence Alignment and Independent Ensembles","abstract":"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.","address":"Tokyo","booktitle":"Proc. of 9th International Conference, SISAP 2016","doi":"10.1007/978-3-319-46759-7_22","editor":[{"propositions":[],"lastnames":["Amsaleg"],"firstnames":["Laurent"],"suffixes":[]},{"propositions":[],"lastnames":["Houle"],"firstnames":["Michael","E."],"suffixes":[]},{"propositions":[],"lastnames":["Schubert"],"firstnames":["Erich"],"suffixes":[]}],"isbn":"9783319467597","keywords":"computational musicology","mendeley-tags":"computational musicology","pages":"286–300","publisher":"Springer Berlin Heidelberg","url":"http://link.springer.com/10.1007/978-3-319-46759-7_22","bibtex":"@InProceedings{ bountouridis.ea2016-music,\n author = {Bountouridis, Dimitrios and Koops, Hendrik Vincent and\n Wiering, Frans and Veltkamp, Remco C.},\n year = {2016},\n title = {Music Outlier Detection Using Multiple Sequence Alignment\n and Independent Ensembles},\n abstract = {The automated retrieval of related music documents, such\n as cover songs or folk melodies belonging to the same\n tune, has been an important task in the field of Music\n Information Retrieval (MIR). Yet outlier detection, the\n process of identifying those documents that deviate\n significantly from the norm, has remained a rather\n unexplored topic. Pairwise comparison of music sequences\n (e.g. chord transcriptions, melodies), from which outlier\n detection can potentially emerge, has been always in the\n center of MIR research but the connection has remained\n uninvestigated. In this paper we firstly argue that for\n the analysis of musical collections of sequential data,\n outlier detection can benefit immensely from the\n advantages of Multiple Sequence Alignment (MSA). We show\n that certain MSA-based similarity methods can better\n separate inliers and outliers than the typical similarity\n based on pairwise comparisons. Secondly, aiming towards an\n unsupervised outlier detection method that is data-driven\n and robust enough to be generalizable across different\n music datasets, we show that ensemble approaches using an\n entropy-based diversity measure can outperform supervised\n alternatives.},\n address = {Tokyo},\n booktitle = {Proc. of 9th International Conference, SISAP 2016},\n doi = {10.1007/978-3-319-46759-7_22},\n editor = {Amsaleg, Laurent and Houle, Michael E. and Schubert,\n Erich},\n isbn = {9783319467597},\n keywords = {computational musicology},\n mendeley-tags= {computational musicology},\n pages = {286--300},\n publisher = {Springer Berlin Heidelberg},\n url = {http://link.springer.com/10.1007/978-3-319-46759-7_22}\n}\n\n","author_short":["Bountouridis, D.","Koops, H. V.","Wiering, F.","Veltkamp, R. C."],"editor_short":["Amsaleg, L.","Houle, M. 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