Real-time Generation of Percussive Rhythms Using Descriptors

A fundamental challenge in computational music generation lies in developing control interfaces that provide intuitive, musically meaningful interactions with generative systems. This thesis addresses this challenge specifically for rhythmic generation, focusing on the development of a system capable of generating 16-step monophonic rhythmic patterns in real time using musically intuitive controls.

Our method uses perceptually grounded rhythmic descriptors as an expressive, intuitive control space. A neural network is trained on all possible binary 16-step  monophonic patterns, learning to map from descriptor space back to rhythmic patterns. We compare this descriptor-based approach to a variational autoencoder model and find the former more effective for usability and expressive control. An interactive interface is developed for exploration and testing, followed by quantitative and  qualitative experiments evaluating the smoothness and user intuitiveness of the system.

Findings show that the descriptor-based model aligns well with listener perception, balancing usability with expressive flexibility. While limited to monophonic rhythms, the system establishes descriptors as a strong foundation for extending interactive rhythm generation to polyphonic and more complex domains.