  We consider the problem of reconstructing the missing phase information from
spectrogram data $|\mathcal{G} f|,$ with $$ \mathcal{G}f(x,y)=\int_\mathbb{R}
f(t) e^{-\pi(t-x)^2}e^{-2\pi i t y}dt, $$ the Gabor transform of a signal $f\in
L^2(\mathbb{R})$. More specifically, we are interested in domains
$\Omega\subseteq \mathbb{R}^2$, which allow for stable local reconstruction,
that is $$ |\mathcal{G}g| \approx |\mathcal{G}f| \quad \text{in} ~\Omega
\quad\Longrightarrow \quad \exists \tau\in\mathbb{T}:\quad \mathcal{G}g \approx
\tau\mathcal{G}f \quad \text{in} ~\Omega. $$ In recent work [P. Grohs and M.
Rathmair. Stable Gabor Phase Retrieval and Spectral Clustering. Comm. Pure
Appl. Math. (2019)] and [P. Grohs and M. Rathmair. Stable Gabor phase retrieval
for multivariate functions. J. Eur. Math. Soc. (2021)] we established a
characterization of the stability of this phase retrieval problem in terms of
the connectedness of the observed measurements. The main downside of the
aforementioned results is that the similarity of two spectrograms is measured
w.r.t. a first order weighted Sobolev norm. In this article we remove this flaw
and essentially show that the Sobolev norm may be replaced by the $L^2-$norm.
Using this result allows us to show that it suffices to sample the spectrogram
on suitable discrete sampling sets -- a property of crucial importance for
practical applications.
