Interesting question :)
Using spectrograms means you are essentially using images (of frequencies varying over time). I understand the content is in understood like the graph i.e. with axes time and frequency, but as far as the network knows, you are giving it black and white images; assuming your last dimension
(=1) is the channels dimension.
You normally want to take the batch-norm over the features, so it could depend on what you see as a feature. Do you care about the shape of the peaks/troughs of the spectra, or the values encoded in the final channel? The meaning of
axis when used in terms of
BatchNormalization might be a little confusing. Have a look at these explanations and some of the points here.
So as far as pure images go, I would recommend keeping the default
Remember that a 2d convolution operation is looking for spatial correlations over the image itself - the kernel slides from left to right, top to bottom. So mixing that idea with then taking batch norm over your second axis (
frequency) is literally an orthogonal idea.
I don't know if it will work out, but I think it might make most sense if your spectrograms are aligned in time, that is they cover the same nominal time period and so for two of them with the same size, the x-axis has the same scale. Otherwise you would be taking batch-normalization over different time-frames and therefore stirring up temporal information between the samples.