This article presents a study that applies Laban’s Effort theory to detect a musician’s expressive intentions during performance. Laban Effort theory was chosen for its capacity to support the observation, description, and interpretation of expressive movement. The aim was to develop interactive musical systems that learn from the embodied expressivity musicians cultivate through practice. The study adopts a multidisciplinary approach, combining Laban Motion Analysis with interactive machine learning. Gesture data, audio, and video were recorded during a performance of Brahms’s Clarinet Sonata Op. 120 No. 2. The video was then analysed by an expert annotator observing Laban Efforts. The annotated video was then reviewed in collaboration with the performer to incorporate Effort Phrasing that reflects changes in intensity within Efforts. The annotations were then used as training data, together with motion data recorded during the performance, to train a regression model. The model was evaluated against expert annotations and through qualitative video analysis. Unlike the very linear notation of a Laban analyst, the model reflects the dynamism of human motion by capturing the almost humanly unobservable nuances in Effort variations. This points to the possibility of using machine learning models to reflect a performer’s expressive intentions in real-time.
Ek et al. (Tue,) studied this question.