|
This code calculates the (windowed!) running mean and variance as well asthe windowed Gaussian surprise for each newly added element.A simple (and useless) usage example: data=rand(1,100); % randomly generate 100 1-D samples history_length=10; % use a window size of 10 samples [s1,m1,v1]=gaussian_windowed_surprise_ring_buffer(data,history_length);where m1 is the running window mean, v1 is the variance in the window,and s1 is the windowed Gaussian surprise (novelty, saliency,interestingness, wow, ...).Here is another example on how we can (very simply) calculate theauditory surprise / acoustic surprise similar to the approach presentedin [1]: T = 0:0.001:2; X = chirp(T,100,1,200,'q'); Y=spectrogram(X,128,120,128,1E3); Z=abs(Y); surprise=mean(gaussian_windowed_surprise_ring_buffer(Z,10)); figure('name','Demo: Surprise of the chirp signal'); plot(surprise);However, in real applications you need to choose, e.g., a meaningfulfrequency range and window length. Furthermore, please consider that thevalues for the first elements (i.e., the first history_size-1 elements)are wrong/broken and should be treated accordingly.Please see [1] for details and an application of the Gaussian windowedsurprise and be so kind to cite [1], if you use the provided code.[1] B. Schauerte, B. KdoDshn, K. Kroschel, R. Stiefelhagen, "Multimodal Saliency-based Attention for Object-based Scene Analysis," in Proc. Int. Conf. Intelligent Robots and Systems (IROS), 2011.(Please note that this is NOT the [reference/original] implementation that was applied in [1] and there ARE differences in the results due to different calculations/algorithms.)
|
Windows Software
Albina Merfyn - I find this to be really easy to use. I can...
