Segmentation in singer turns with the Bayesian information criterion

As part of a project on indexing ethno-musicological audio recordings, segmentation in singer turns automatically appeared to be essential. In this article, we present the problem of segmentation in singer turns of musical recordings and our first experiments in this direction by exploring a method based on the Bayesian Information Criterion (BIC), which are used in numerous works in audio segmentation, to detect singer turns. The BIC penalty coefficient was shown to vary when determining its value to achieve the best performance for each recording. In order to avoid the decision about which single value is best for all the documents, we propose to combine several segmentations obtained with different values of this parameter. This method consists of taking a posteriori decisions on which segment boundaries are to be kept. A gain of 7.1% in terms of F-measure was obtained compared to a standard coefficient.