Segmentation of music signals based on explained variance ratio for applications in spectral complexity reduction
Ekaterina A. Krymova, Anil M. Nagathil, Denis Belomestny, Rainer Martin
Abstract
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.
BibTeX
@inproceedings{icassp2017_segmentationofmu,
title = {Segmentation of music signals based on explained variance ratio for applications in spectral complexity reduction},
author = {Ekaterina A. Krymova and Anil M. Nagathil and Denis Belomestny and Rainer Martin},
booktitle = {ICASSP 2017},
year = {2017}
}