Design and optimization of neuro-fuzzy-based recognition of musical rhythm patterns

Since melody is based on rhythm, the task of recognizing patterns and assigning rhythmic structure to unquantized musical input is a fundamental one for interactive musical systems and for searching musical databases. We use a combination of combinatorial pattern matching and structural interpretation with a match quality rating by a neuro-fuzzy system that incorporates musical knowledge and operates on perceptually relevant features extracted from the input data. This system can learn from relatively few expert examples by using iterative training by relative samples. It shows good recognition results and the used methods of pre-filtering and optimization facilitate efficient computation. The system is modular, so feature extraction, rules, and perceptual constraints can be changed to adapt it to other areas of application.