Gaussian Surprise and Running Windowed Mean / Variance
Updated 7 Mar 2013
This code calculates the (windowed!) running mean and variance as well as
the (windowed) Gaussian surprise for each newly added element. Furthermore,
it is possible to calculate the univariate Gamma surprise.
The code comes with two examples on acoustic saliency/surprise using the
(synthetic) chirp signal and a real-world, meeting room audio recording.
Please see  for details and an application of the Gaussian windowed
surprise. See  for details on the univariate Gamma model. Be so kind
to cite  and/or , if you use the provided code.
 B. Schauerte, B. Kuehn, K. Kroschel, R. Stiefelhagen, "Multimodal
Saliency-based Attention for Object-based Scene Analysis," in Proc.
Int. Conf. Intelligent Robots and Systems (IROS), 2011.
 B. Schauerte, R. Stiefelhagen, ""Wow!" Bayesian Surprise for Salient
Acoustic Event Detection". In Proc. 38th Int. Conf. Acoustics,
Speech, and Signal Processing (ICASSP), 2013.
Boris Schauerte (2023). Gaussian Surprise and Running Windowed Mean / Variance (https://www.mathworks.com/matlabcentral/fileexchange/33573-gaussian-surprise-and-running-windowed-mean-variance), MATLAB Central File Exchange. Retrieved .
MATLAB Release Compatibility
Platform CompatibilityWindows macOS Linux
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!Start Hunting!
Discover Live Editor
Create scripts with code, output, and formatted text in a single executable document.
- added the univariate Gamma model
- added two examples
improved the documentation and added an example script
- added a help
- set the numerically more stable variance estimation as default