Real-time Analysis of Interactive Scores in PWGL

In this article, we introduce an original approach to computerized music analysis within the graphical computer-assisted composition environment called PWGL. Our aim is to facilitate real-time analysis of interactive scores written in common Western music notation. To this end, we have developed a novel library that allows us to analyze scores realized with the help of ENP, and to visualize the results of the analysis in real-time. ENP is the native music notation tool of PWGL able to produce automatically typeset and interactive music notation. Here, it is extended to support the display of analytical information that can be drawn on top of the score as an overlay. The analysis backend is realized with the help of our built-in musical scripting language. The language is based on pattern-matching and allows for a rich access of score information. The results of the analysis are presented directly as a part of the original score leveraging the extensible and interactive visualization capabilities of ENP

Machine learning of symbolic compositional rules with genetic programming: dissonance treatment in Palestrina

We describe a method for automatically extracting symbolic compositional rules from music corpora. Resulting rules are expressed by a combination of logic and numeric relations, and they can therefore be studied by humans. These rules can also be used for algorithmic composition, where they can be combined with each other and with manually programmed rules. We chose genetic programming (GP) as our machine learning technique, because it is capable of learning formulas consisting of both logic and numeric relations. GP was never used for this purpose to our knowledge. We therefore investigate a well understood case in this study: dissonance treatment in Palestrina’s music. We label dissonances with a custom algorithm, automatically cluster melodic fragments with labelled dissonances into different dissonance categories (passing tone, suspension etc.) with the DBSCAN algorithm, and then learn rules describing the dissonance treatment of each category with GP. Learning is based on the requirement that rules must be broad enough to cover positive examples, but narrow enough to exclude negative examples. Dissonances from a given category are used as positive examples, while dissonances from other categories, melodic fragments without dissonances, purely random melodic fragments, and slight random transformations of positive examples, are used as negative examples