Segmentation of music signals based on explained variance ratio for applications in spectral complexity reduction

Since natural acoustic signals like speech or music exhibit a highly varying temporal structure, signal enhancement and feature extraction algorithms benefit from segmentation procedures which take the underlying signal structure into account. In this paper we present a novel unsupervised segmentation procedure for music signals which relies on an explained variance criterion in the eigenspace of the constant-Q spectral domain. The procedure is used in the context of a spectral complexity reduction method which mitigates effects of cochlear hearing loss. It is compared to a segmentation based on equidistant boundaries. The results demonstrate that the proposed segmentation procedure gives an improvement in terms of signal-to-artefacts ratio in comparison to corresponding equidistant boundaries segmentation.