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I am trying to compute the Wigner Ville Distribution for short audio clips. While it works correctly with small premade signals, the situation changes for wav files. The kernel crashes due to memory error. Huge arrays are needed when a NumPy array of zeroes is initiated. I can bypass this by downsampling my signal. The most I can get is around 20000 samples. More samples and the kernel dies due to the memory error. Am I doing something wrong or is the implementation that memory hungry?
The text was updated successfully, but these errors were encountered:
Hi @amChristonasis - Sorry for the delay in responding. The WignerVille class is indeed memory intensive, but I'm sure some optimizations can be made to the implementation. I'll take a look over the next couple of weeks.
Thanks a lot for the reply. I actually finished with my work on Wigner-Ville but I would still be interested to see how the implementation could be made faster.
Cheers,
Antonios
I am trying to compute the Wigner Ville Distribution for short audio clips. While it works correctly with small premade signals, the situation changes for wav files. The kernel crashes due to memory error. Huge arrays are needed when a NumPy array of zeroes is initiated. I can bypass this by downsampling my signal. The most I can get is around 20000 samples. More samples and the kernel dies due to the memory error. Am I doing something wrong or is the implementation that memory hungry?
The text was updated successfully, but these errors were encountered: