@Mole_Turner A lot of the more recent models I’ve read about don’t resort to using a time-frequency (T-F) representation of the input wave signal (e.g. STFT representation). Also, I read in Supervised Speech Separation Based on Deep Learning: An Overview 2 that end-to-end speech separation methods like temporal mapping (which doesn’t require resorting to a T-F representation) has the following advantage:

A potential advantage of this approach is to circumvent the need to use the phase of noisy speech in reconstructing enhanced speech, which can be a drag for speech quality, particularly when input SNR is low.

and that

As a convolution operator is the same as a filter or a feature extractor, CNNs appear to be a natural choice for temporal mapping.