Enhancing Gesture Recognition for Musical Conducting: A Study on Diverse Data Classification and Stacked Neural Network Architectures

This study addresses the limitations of many gesture recognition algorithms, which predominantly employ machine learning-based approaches tailored to specific types of gestures, leaving niche gestures such as musical conducting gestures largely unexplored. To advance the research in musical conducting gesture recognition, we focus on two key aspects: (1) broadening the dataset to encompass various conducting speeds and investigating its impact on performance, and (2) introducing a stacked neural network architecture to explore performance improvements beyond conventional node increase. The study demonstrates that incorporating diverse data signifi- cantly enhances performance and that stacking neural network layers yields notable performance gains.