Gaussian Surprise and Running Windowed Mean / Variance

Compute the Gaussian/Gamma surprise/saliency of data as well as its windowed mean/variance
Updated 7 Mar 2013

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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 [1] for details and an application of the Gaussian windowed
surprise. See [2] for details on the univariate Gamma model. Be so kind
to cite [1] and/or [2], if you use the provided code.

[1] 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.

[2] B. Schauerte, R. Stiefelhagen, ""Wow!" Bayesian Surprise for Salient
Acoustic Event Detection". In Proc. 38th Int. Conf. Acoustics,
Speech, and Signal Processing (ICASSP), 2013.

Cite As

Boris Schauerte (2024). Gaussian Surprise and Running Windowed Mean / Variance (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2011a
Compatible with any release
Platform Compatibility
Windows macOS Linux
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Version Published Release Notes

- added the univariate Gamma model

- added two examples

improved the documentation and added an example script

- added a help
- minor C/C++ interface changes

- set the numerically more stable variance estimation as default
- minor interface adaptation of the C/C++ code