Evaluation of Kernel Methods for Change Detection and Segmentation : Application to Audio Onset Detection
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.
AT THIS PAGE YOU CAN DOWNLOAD THE WHOLE ESSAY. (follow the link to the next page)