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-30 dB is still very noisy. If you've had success with EMD, I'd try an inspired transform that's improved on it: synchrosqueezing. Whether it's best to denoise before classifying depends on amount of available data: most denoising will throw away some valuable information, but also make the task easier for a classifier. If there's lots of data, don't denoise....


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Basically, if we define convolution as $ y = h \ast x $, it can be written in Matrix form (See Generate the Matrix Form of 1D Convolution Kernel): $$ \boldsymbol{y} = H \boldsymbol{x} $$ Transposed Convolution is given by: $$ {H}^{T} \boldsymbol{z} $$ If you look carefully, you'd see the spatial operation is basically correlation instead of convolution (...


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