Onset Detection through Maximal Redundancy Detection

We propose a criterion, called `maximal redundancy', for onset detection in time series. The concept redundancy is adopted from information theory and indicates how well a signal locally can be explained by an underlying model. It is shown that a local maximum in the redundancy is a good indicator for an onset. It is proven that `maximal redundancy' detection is a statistical asymptotically optimal detector for AR processes. It also accounts for potentially non-Gaussian time series and nonGaussian innovations in the AR processes. Several applications are shown where the new criterion has been successfully applied.

• Type: Conference
• Year: 2006
• Where: ICPR
• Authors: Gert Van Dijck, Marc M. Van Hulle
• Keywords: AR Processes, Computer Vision, Concept Redundancy, ICPR 2006, Maximal Redundancy