I have audio data acquired from a 4 channels sensors array.
As a preprocessing step for a neural network, I want to beamform and focus on the sound source. For higher resolution in the beamforming process, I have upsampled the signal with a factor of 5 using cubic interpolation, from 48 kHz to 240 kHz.
I am aware that now my network will train much slower due to the longer vectors. Nevertheless, my network is not performing well (it did without preprocessing step). Is there any connection? Is there any reason to downsample the frequency back for machine learning purposes specifically and for classical signal processing method in general?