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In PCA, featVector is defined as a matrix of size 1 x inputFeatureSize,
essentia/src/algorithms/highlevel/pca.cpp
Line 93 in 2de0185
| Array2D<Real> featVector(1,bands, 0.0); |
However, in this line
featVector is implicitly resized to 1 x outputFeatureSize,essentia/src/algorithms/highlevel/pca.cpp
Line 99 in 2de0185
| featVector = matmult(featVector, reducedEig); |
This causes an invalid memory access on the second iteration of the loop when inputFeatureSize != outputFeatureSize.
To reproduce the error set the parameter dimensions to anything smaller than the input feature size on any PCA routine.
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