Efficient audio segmentation algorithms based on the BIC

A widely adopted algorithm for the audio segmentation is based on the Bayesian information criterion (BIC), applied within a sliding variable-size analysis window. In this work, three different implementations of that algorithm are analyzed in detail: (i) one that keeps updated a pair of sums, that of input vectors and that of square input vectors, in order to save computations in estimating covariance matrixes on partially shared data; (ii) one, recently proposed in the literature, that exploits the encoding of the input signal with cumulative statistics for the efficient estimation of covariance matrixes; and (iii) an original one, that encodes the input stream with the cumulative pair of sums of the first approach. The three approaches have been compared both theoretically and experimentally, and the proposed original approach is shown to be the most efficient.