Representing Musical Sounds With an Interpolating State Model

Abstract

A computationally efficient algorithm is proposed for modeling and representing time-varying musical sounds. The aim is to encode individual sounds and not the statistical properties of several sounds representing a certain class. 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 proposed interpolating state model is generic and can be used to represent any multidimensional data sequence. In this paper, it is applied to represent musical instrument sounds in a compact and accurate form. Simulation experiments were carried out which show that the proposed method clearly outperforms the conventional vector quantization approach where the acoustic feature data is k-means clustered and the feature vectors are replaced by the corresponding cluster centroids. The computational complexity of the proposed algorithm as a function of the input sequence length T is O(T log T).