Journal of the Alamire Foundation

The anonymous ballade Ariere tost is one of the most chromatic songs from the late fourteenth and early fifteenth centuries, featuring a variety of accidentals matched only by the more widely-known Fumeux fume and Le mont Aon. In an attempt to arrive at a composer attribution, the song was examined using a variety of different methods, the main findings of which are as follows. Firstly, the results consistently indicate an origin for the song at the court of Jean de Berry and Johannes Cesaris as its composer, an attribution supported by musical material it shares with his A l’aventure va Gauvain and Je ris, je chante. Secondly, the results strongly suggest that Ariere tost is a self-reflexive satire of songs written for the 1389 wedding of Jean de Berry and Jeanne de Boulogne that incorporates multiple stylistic elements associated with these songs, both textual and musical, in order to illustrate and comment on the techniques of musical flattery. Thirdly, statistical, textual, and shared musical characteristics, along with poetic and documentary evidence, suggest that Cesaris’s Ariere tost, Bonté bialté, Je ris, je chante, and possibly A l’aventure va Gauvain, are contemporaneous with composition dates c. 1390. Finally, the fact that the majority of these chromatic works were written by composers associated with the court of Jean de Berry strongly suggests that this was a stylistic trait cultivated and favoured there.

In this paper, we propose a novel clustering method to represent and compare works of symbolic music, in particular the music in the database of the Josquin Research Project. We also study the results of our methods on the database as a whole, as well as the results for some specific cases. The methods that we propose revolve around modelling the activity of the twelve pitch classes (i.e., chromae) over time. We suggest that by modelling this activity, we can capture the harmonic progressions in the music. By converting the models to a vector representation, we can analyse the works in the dataset using clustering techniques from machine learning. These techniques can be used to group, visualize, and classify works in the corpus. This way, we suggest attributions for insecurely attributed pieces in the dataset, based solely on the harmonic progressions contained in the songs. We also apply our final classification models to the unica of the Leuven Chansonnier, to give the first suggestions for the attribution of these works from the world of computational musicology. We then compare our methods to some existing methods for analyzing symbolic music, and find that our method is slightly worse at identifying composers of Renaissance works. This result was not completely unexpected, as the features we extract are based only the musical progressions present in the music. Finally, we use the best vector representation we devised together with unsupervised dimensionality reduction algorithms to create a scatterplot visualization of the entire Josquin Research Project dataset.