LukeIt’s Friday and in an attempt to remember what it was like when I did music cognition research here is possibly the article I am proudest of: it was a huge challenge to work on but I think it was a pretty good stab at better understanding a complex human behaviour AND testing some algorithmic approaches to solving a musical problem. Hank and I nearly came to blows because of our radically different backgrounds and personality types but all was well in the end. Fortunately Peter was good at keeping us on track ;-)
https://link.springer.com/article/10.3758/BF03200827#preview
(no paywall)
#music #InformationProcessing #PatternMatching #algorithms #modeling #CognitiveScience #MusicTechnology #memories
Data processing in music performance research: Using structural information to improve score-performance matching - Behavior Research Methods
In order to study aspects of music performance, one has to find correspondences between the performance data and a score. Locating the corresponding score note for every performance note, calledmatching, is therefore a common task. An algorithm that automates this procedure is called amatcher. Automated matching is difficult because performers make errors, performers use expressive timing, and scores are frequently underspecified. To find the best match, most matchers use information about pitch, temporal order, and the number of matched notes. We show that adding information about the musical structure of the score gives better results. However, we found that even this information was insufficient to identify some types of performance errors and that a definition of best match based only on the number of matched notes is sometimes problematic. We provide some suggestions about how to achieve greater improvements.
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