Evaluation of BIC-based algorithms for audio segmentation

The Bayesian Information Criterion (BIC) is a widely adopted method for audio segmentation, and has inspired a number of dominant algorithms for this applica- tion. At present, however, literature lacks in analytical and experimental studies on these algorithms. This paper tries to partially cover this gap. Typically, BIC is applied within a sliding variable-size analysis window where single changes in the nature of the audio are locally searched. Three different imple- mentations of the algorithm are described and compared: (i) the first keeps updated a pair of sums, that of input vectors and that of square input vectors, in order to save computations in estimating covariance matrices on partially shared data; (ii) the second implementation, recently proposed in literature, is based on the encoding of the input signal with cumulative statistics for an efficient estimation of covariance matrices; (iii) the third implementation consists of a novel approach, and is char- acterized by the encoding of the input stream with the cumulative pair of sums of the first approach. Furthermore, a dynamic programming algorithm is presented that, within the BIC model, finds a globally optimal segmentation of the input audio stream. All algorithms are analyzed in detail from the viewpoint of the computational cost, experimentally evaluated on proper tasks, and compared.