Applying CNN to Fast time Fourier Transform?

I have data that fast time fourier transform is applied. (amplitudes at specific Hzs)

There are solutions on internet that CNN is applied to mel spectrogram, however, I see no solution that CNN is applied to Fast Fourier Transformed signal.

Is it possible that CNN is applied to Fast Fourier Transformed signals?

Or is it not possible because CNN is considering temporal attribute?

Thanks!

I'm assuming each row of your spreadsheet is IID, e.g. it wouldn't change the problem to re-order the rows in that spreadsheet.

In this case you have a pretty typical ML problem. The fact that the FFT has already been applied and specific frequency responses (columns) have been extracted is a process called "feature engineering". Prior to the common use of neural networks, this was a standard step in all machine learning problems and remains common to a great many domains.

With data that has been feature engineered, you should look to traditional ML algorithms. Random Forests, XGBoost, and Linear Regression come to mind. A fully connected neural network is also appropriate, but I would typically expect it to under-perform other ML methods.

The hallmark of a CNN is that it operates on an ordered sequence of data. In your case the raw data, from which your dataset was derived, would be appropriate for a CNN. In a sound file you have a 1D sequence of information. You could not re-order the data in the time dimension without fundamentally changing its meaning.

A 2D CNN operates over an image where the pixel order in X and Y cannot be changed. Again the sequential order of the data matters. The same applies for 3D CNNs.

Be aware that the application of a FFT has fundamentally biased your solution by representing it only in a limited set of frequency responses. All feature engineering is fundamentally biasing the problem, presumably in a well thoughout-out way. However, it's entirely possible that other useful signals in the data exist, which aren't expressed by the FFT @ 10, 20, 30 Hz, etc. The CNN has the capacity to learn its own version of an FFT as well as other non cyclic patterns. Typically, the lack of a feature engineering step is the key differentiator between the CNN and traditional ML algorithms.