So, In my report I am comparing the related works, In most of the previous work, researchers have used spectrogram as input to vanilla CNN and classify. Where I am using different handcrafted features from the signal and then feeding it to DNN.
I want to explain why the second method works better than first, for that I am comparing like this
Motivated by prior work, Several AI-based approaches have been proposed to classify sounds using signal analysis. Most of them use short-term magnitude spectrograms transformed from sound data as input to Convolutional neural network (CNN). The drawback of this method is, Since CNN is based only on a spectrogram input, some domain-specific important characteristics might get missed in the feature space. Such as Zero crossing rate(ZCR), Skewness, Shannon Entropy, Fundamental frequency (F0), Formant frequencies etc
I just wanted to know, Is this statement correct from DSP aspect? because I am saying in my statement that spectrogram doesn't contain zero-crossing rate(ZCR), Skewness, Shannon Entropy, Fundamental frequency (F0), Formant frequencies etc