Evaluation of Kernel Methods for Change Detection and Segmentation : Application to Audio Onset Detection

University essay from Uppsala universitet/Institutionen för informationsteknologi

Author: Alexandre Lung-yut-fong; [2008]

Keywords: ;

Abstract:

Finding changes in a signal is a pervasive topic in signal processing. Through the example of audio onset detection to which we apply an online framework, we evaluate the ability of a class of machine learning techniques to solve this task.

The goal of this thesis is to review and evaluate some kernel methods for thetwo-sample problem (one-class Support Vector Machine, Maximum MeanDiscrepancy and Kernel Fisher Discriminant Analysis) on the change detection task, by benchmarking our proposed framework on a set of annotated audio files to which we can compare our results to the ground-truth onset times.

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