We investigate the retrieval of a binary time-frequency mask from a few observations of filtered white ambient noise. Confirming household wisdom in acoustic modeling, we show that this is possible by inspecting the average spectrogram of ambient noise. Specifically, we show that the lower quantile of the average of $\mathcal{O}(\log(|\Omega|/\varepsilon))$ masked spectrograms is enough to identify a rather general mask $\Omega$ with confidence at least $\varepsilon$, up to shape details concentrated near the boundary of $\Omega$. As an application, the expected measure of the estimation error is dominated by the perimeter of the time-frequency mask. The estimator requires no knowledge of the noise variance, and only a very qualitative profile of the filtering window, but no exact knowledge of it.

翻译：我们研究从少量滤波白环境噪声观测中恢复二元时频掩码的问题。证实了声学建模中的普遍经验，我们表明通过检查环境噪声的平均谱图可以实现这一目标。具体而言，我们证明，对于平均 $\mathcal{O}(\log(|\Omega|/\varepsilon))$ 个掩蔽谱图的下分位数，足以以至少 $\varepsilon$ 的置信度识别相当一般的掩码 $\Omega$，仅忽略集中在 $\Omega$ 边界附近的形状细节。作为应用，估计误差的期望度量由时频掩码的周长主导。该估计器无需知晓噪声方差，且仅需滤波窗口的非常定性的轮廓信息，而无需其精确知识。