Efficacy Score for Data Augmentations

版本 1.0.2 (2.1 KB) 作者: David Heise

A function for calculating the efficacy score, or eff-score, of a data augmentation prior to training and empirical testing of a model.

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更新时间 2023/8/20

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This function will calculate the eff-score of a given augmentation as described in:

Heise, D. and Bear, H, "A Transparent Method for Visualizing and Scoring the Efficacy of Data Augmentations on Real-World Audio", submitted to IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, in review, 2023.

Inputs:

D - a square dissimilarity matrix, computed via the Visual Assessment of cluster Tendency (VAT) algorithm, of all the samples in the source data

k - an integer specifying the size of neighbourhoods (in pixels) to use for computing diversity (optional; by default, k = 3)

b - an integer specifying the number of class intervals to use for binning the set of diversity values, such as for producing a histogram (optional; by default, b = 20)

w - an integer specifying the width of each class interval (optional, computed automatically if not specified); w should be chosen such that (b*w) is just greater than the maximum diversity

Outputs:

score - the computed eff-score

b_out - the value of b used by the function

w_out - the value of w used by the function

h_fig - a handle to the produced histogram figure

引用格式

D. Heise and H. Bear, "Evaluating the Potential and Realized Impact of Data Augmentations", submitted to 2023 IEEE Symposium Series on Computational Intelligence, in review.