Structural segmentation of Hindustani concert audio with posterior features

Structural segmentation of music involves identifying boundaries between homogenous regions where the homogeneity involves one or more musical dimensions, and therefore depends on the musical genre. In this work, we address the segmentation of Hindustani instrumental concert recordings at the highest time-scale, that is, concert sections marked by prominent changes in rhythmic structure. Tempo features are effectively combined with energy and chroma features motivated by musicological knowledge and acoustic observations. Posterior probability features from unsupervised model fitting of the frame-level acoustic features are shown to significantly improve robustness to local acoustic variations. Finally, two diverse change detection criteria are combined to obtain a superior segmentation system.