Neural Network: Spectrogram Dimension I like to work with a convolutional neural network in combination with a spectrogram as input.

Assuming the spectrogram has the dimensions $T\times F$ (time$\times$frequency). Is it more natural to see the spectrogram as a 1D image with $F$ channels and the size $T\times 1$ or as a 2D image with the size $T\times F$?

Depending on this I work with 1D convolutions or with 2D convolutions. But I'm not sure whats the more natural way.

Thank you very much
Depends on on what kind of feature(s) you want your CNN trained.  With 2D convolution, convolution in the F dimension of a T-F spectrogram will produce something like a real cepstrum, which has proved useful in speech recognition and musical pitch detection.