Reconstruction is possible so long as NOLA is obeyed - which is an easier criterion (on synthesis information) to meet than what you seek (analysis information).

To discriminate temporal variations finer than $T$, the window's temporal width must be $\leq T$. You can use ssqueezepy's window_resolution with appropriate unit conversion (mult by $f_s$) to measure.

Further, there's a constraint on "hop length" or "stride". If you seek to capture e.g. A.M. of some frequency that itself has frequency $100\text{ Hz}$, then for $1\text{ sec}$ duration, we require $>200$ samples per row (where otherwise e.g. $50$ may have sufficed), else the A.M. will alias. Note this aliasing is of analysis information and doesn't affect reconstruction.

More importantly, if you're doing instantaneous frequency/amplitude/phase analysis, I recommend trying CWT and synchrosqueezing, often superior to STFT.

Reconstruction is possible so long as NOLA is obeyed - which is an easier criterion (on synthesis information) to meet than what you seek (analysis information).

To discriminate temporal variations finer than $T$, the window's temporal width must be $\leq T$. You can use ssqueezepy's window_resolution with appropriate unit conversion (mult by $f_s$) to measure.

Further, there's a constraint on "hop length" or "stride". If you seek to capture e.g. A.M. of some frequency that itself has frequency $100\text{ Hz}$, then for $1\text{ sec}$ duration, we require $>200$ samples per row (where otherwise e.g. $50$ may have sufficed), else the A.M. will alias. Note this aliasing is of analysis information and doesn't affect reconstruction.

More importantly, for instantaneous frequency/amplitude/phase analysis, I recommend CWT and synchrosqueezing, often superior to STFT.