This note presents a system that learns expressive and idiosyncratic gesture variations for gesture-based interaction. The system is used as an interaction technique in a music conducting scenario where gesture variations drive music articulation. A simple model based on Gaussian Mixture Modeling is used to allow the user to configure the system by providing variation examples. The system performance and the influence of user musical expertise is evaluated in a user study, which shows that the model is able to learn idiosyncratic variations that allow users to control articulation, with better performance for users with musical expertise.

The turn toward the digital has opened up previously difficult to access musical materials to wider musicological scholarship. Digital repositories provide access to publicly licensed score images, score encodings, textual resources, audiovisual recordings, and music metadata. While each repository reveals rich information for scholarly investigation, the unified exploration and analysis of separate digital collections remains a challenge. TROMPA-Towards Richer Online Music Public-domain Archives-addresses this through a knowledge graph interweaving composers, performers, and works described in established digital music libraries, facilitating discovery and combined access of complementary materials across collections. TROMPA provides for contribution of expert insights as citable, provenanced annotations, supporting analytical workflows and scholarly communication. Beyond scholars, the project targets four further user types: instrumental players; choir singers; orchestras; and music enthusiasts; with corresponding web applications providing specialised views of the same underlying knowledge graph. Thus, scholars' annotations provide contextual information to other types of users; while performers' rehearsal recordings and performative annotations, conductors' marked up scores, and enthusiasts' social discussions and listening behaviours, become available to scholarly analysis (per user consent). The knowledge graph is exposed as Linked Data, adhering to the FAIR principles of making data Findable, Accessible, Interoperable, and Re-usable, and supporting further interlinking, re-interpretation and re-use beyond the immediate scope of the project. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored.

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