Welch Transformation to keep the dimensions
I am trying to extract the scipy.signal.welch signal from a temporal 1d time series and since I am not adept in signal processing I have no idea why the dimensions shrink when returned.
I need to concatenate the temporal to the spectral as another channel so if the temporal is of shape:
[batches, channels, sample_length]

then I expect to get after the concatenation:
[batches, 2*channels, sample_length]

but when I try to train my model an error is thrown because the size of the spectral doesn't match the temporal (the temporal size is 16):
size mismatch, m1: [2 x 9], m2: [16 x 16]

I tried to look at the documentation but they don't mention why it shrinks it and how it can be avoided.

As @SleuthEye said, I set the the return_onesided to False like so:
scipy.signal.welch(temporal, axis=-1, return_onesided=False, nperseg=temporal.shape[0])

