Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient greedy learning of Gaussian mixture models
Neural Computation
Modulation-scale analysis for content identification
IEEE Transactions on Signal Processing - Part II
A Multipitch Analyzer Based on Harmonic Temporal Structured Clustering
IEEE Transactions on Audio, Speech, and Language Processing
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In this paper a computational approach of musical orchestration is presented. We consider orchestration as the search of relevant sound combinations within large instruments sample databases. The working environment is Orchidée an evolutionary orchestration algorithm that allows a constrained multiobjective search towards a target timbre, in which several perceptual dimensions are jointly optimized. Up until now, Orchidée was bounded to “time-blind” features, by the use of averaged descriptors over the whole spectrum. We introduce a new instrumental model based on Gaussian Mixture Models (GMM) which allows to represent the complete spectro-temporal structure. We then present the results of the integration of our model and improvement that it brings to the existing system.