Modeling musical sounds with an interpolating state model

A computationally efficient algorithm is proposed for modeling and coding the time-varying spectra of musical sounds. The aim is to encode individual data sets and not the statistical properties of the sounds. A given sequence of acoustic feature vectors is modeled by finding such a set of “states” (anchor points in the feature space) that the input data can be efficiently represented by interpolating between them. The achieved modeling accuracy for a database of musical sounds was approximately two times better than that of a conventional “vector quantization” model where the input data was k-means clustered and the input data vectors were then replaced by their corresponding cluster centroids. The computational complexity of the proposed algorithm as a function of the input sequence length T is O(TlogT).