Luca Andrea Ludovico

The Ricordi archive, a prestigious collection of significant musical manuscripts from renowned opera composers such as Donizetti, Verdi and Puccini, has been digitized. This process has allowed us to automatically extract samples that represent various musical elements depicted on the manuscripts, including notes, staves, clefs, erasures, and composer’s annotations, among others. To distinguish between digitization noise and actual music elements, a subset of these images was meticulously grouped and labeled by multiple individuals into several classes. After assessing the consistency of the annotations, we trained multiple neural network-based classifiers to differentiate between the identified music elements. The primary objective of this study was to evaluate the reliability of these classifiers, with the ultimate goal of using them for the automatic categorization of the remaining unannotated data set. The dataset, complemented by manual annotations, models, and source code used in these experiments are publicly accessible for replication purposes.1

In recent years, Accessible Digital Musical Instruments (ADMIs) designed for motor-impaired individuals that incorporate gaze-tracking technologies have become more prevalent. To ensure a reliable user experience and minimize delays between actions and sound production, interaction methods must be carefully studied. This paper presents Kiroll, an affordable and open-source software ADMI specifically designed for quadriplegic users. Kiroll can be played by motor-impaired users through eye gaze for note selection and breath for sound control. The interface features the infinite keyboards context-switching interaction method, which exploits the smooth-pursuit capabilities of human eyes to provide an indefinitely scrolling layout so as to resolve the Midas Touch issue typical of gaze-based interaction. This paper outlines Kiroll’s interaction paradigm, features, implementation processes, and design approach.