Making music through real-time voice timbre analysis
The central contribution of this work is the application of supervised and unsupervised machine learning techniques to automatically map vocal timbre to synthesiser timbre and controls. Component contributions include a delayed decision-making strategy for low-latency sound classification, a regression-tree method to learn associations between regions of two unlabelled datasets, a fast estimator of multidimensional differential entropy and a qualitative method for evaluating musical interfaces based on discourse analysis.