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ICASSP
2009
IEEE

A regularized kernel-based approach to unsupervised audio segmentation

10 years 4 months ago
A regularized kernel-based approach to unsupervised audio segmentation
We introduce a regularized kernel-based rule for unsupervised change detection based on a simpler version of the recently proposed kernel Fisher discriminant ratio. Compared to other kernel-based change detectors found in the literature, the proposed test statistic is easier to compute and has a known asymptotic distribution which can effectively be used to set the false alarm rate a priori. This technique is applied for segmenting tracks from TV shows, both for segmentation into semantically homogeneous sections (applause, movie, music, etc.) and for speaker diarization within the speech sections. On these tasks, the proposed approach outperforms other kernel-based tests and is competitive with a standard HMM-based supervised alternative.
Zaïd Harchaoui, Felicien Vallet, Alexandre Lu
Added 21 May 2010
Updated 21 May 2010
Type Conference
Year 2009
Where ICASSP
Authors Zaïd Harchaoui, Felicien Vallet, Alexandre Lung-Yut-Fong, Olivier Cappé
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