Seminars

The emergence of musical patterns via repetition/similarity is paramount in making sense and understanding music. Yet, despite the efforts made towards its systematic description, musical similarity remains an elusive concept, resisting robust formalisation. Why does the introduction of well-established powerful pattern matching techniques (exact or approximate) in the musical domain, usually ends up with rather limited/partial/fragmentary results? Why is it so difficult to create a general model of musical similarity that may capture musically and cognitively plausible patterns? In this presentation, we will focus on three sources of difficulty in describing musical similarity. Firstly, it is not always easy, to get a musical sequence per se on which to apply pattern matching techniques; especially in non-monophonic music (i.e., most music), it is anything but trivial to derive cognitively meaningful auditory images/streams within which patterns may emerge. Secondly, it is most important to decide how a coherent sequence of musical entities may be represented; representation in music is complex due to the multi-dimensional and hierarchic nature of musical data. Thirdly, it is vital to define the nature of a certain similarity process, as special models may have to be devised (rather than use of standard off-the- shelf algorithms). In this presentation, examples and techniques from recent research on musical pattern discovery, in melodic, harmonic and rhythmic contexts, will be presented to highlight the importance of looking in detail at the musical and cognitive aspects of music pattern discovery tasks before attempting to use/develop specific pattern matching algorithms.

Emilios Cambouropoulos is Associate Professor in Musical Informatics at the School of Music Studies, Aristotle University of Thessaloniki. He studied Physics, Music, and Music Technology before obtaining his PhD in 1998 on Artificial Intelligence and Music at the University of Edinburgh. He worked as a research associate at King’s College London (1998-1999) on a musical data-retrieval project and was employed at the Austrian Research Institute for Artificial Intelligence (OeFAI) in Vienna on the project Artificial Intelligence Models of Musical Expression (1999-2001). Recently he was principal investigator for the EU FP7 project Concept Invention Theory COIVENT (2013-2016). His research interests cover topics in the domain on cognitive and computational musicology (CCM Group - ccm.web.auth.gr) and has published extensively in this field in scientific journals, books and conference proceedings. Homepage: http://users.auth.gr/emilios/