Python audio analysis: which spectrogram should I use and why?
I recommend a Synchrosqueezed Continuous Wavelet Transform representation, available in ssqueezepy. Synchrosqueezing arose in context of audio processing (namely speaker identification), and there's much literature on applying CWT for audio tasks.
Advantage over STFT is the inherently logarithmic nature of the feature extractor, matching audio structuring. That is, we perceive sound differences in relative terms, so 6kHz is to 3kHz what 24kHz is to 12kHz. STFT is unable to map such features without being very wasteful, as it lacks an adaptive window (your Spectrogram A is not logarithmic; it's a logarithmic display of linear data). Analytic CWT (CWT with analytic wavelet) further enjoys superior instantaneous frequency and amplitude representations (see below references).
Synchrosqueezing further enhances these representations, and can be thought of as an attention mechanism. Experiments have shown it to enhance EEG classifier performance, for example. Currently only CWT is supported, but I'll be adding STFT within a week or so. Also see:

Excellent CWT tutorial (also comparing with STFT)
Synchrosqueezing friendly overview
CNN + CWT vs STFT comparison
Synchrosqueezing paper


Examples on EEG data: