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Structural segmentation of musical audio signals is one of many active areas of Music Information Retrieval (MIR) research. One aspect of this important topic which has so far received little attention though is the potential advantage to be gained by utilizing multitrack audio. This paper gives an overview of current segmentation techniques, and demonstrates that by applying a particular segmentation algorithm to multitrack data, rather than the usual case of fully mixed audio, we achieve a significant and quantifiable increase in accuracy when locating segment boundaries. Additionally, we provide details of a structurally annotated multitrack test set available for use by other researchers.

We propose two speech/music discrimination methods using timbre models and measure their performances on a 3 hour long database of radio podcasts from the BBC. In the first method, the machine estimated classifications obtained with an automatic timbre recognition (ATR) model are post-processed using median filtering. The classification system (LSF/K-means) was trained using two different taxonomic levels, a high-level one (speech, music), and a lower-level one (male and female speech, classical, jazz, rock & pop). The second method combines automatic structural segmentation and timbre recognition (ASS/ATR). The ASS evaluates the similarity between feature distributions (MFCC, RMS) using HMM and soft K-means algorithms. Both methods were evaluated at a semantic (relative correct overlap RCO), and temporal (boundary retrieval F-measure) levels. The ASS/ATR method obtained the best results (average RCO of 94.5% and boundary F-measure of 50.1%). These performances were favourably compared with that obtained by a SVM-based technique providing a good benchmark of the state of the art.