Unbiased MMSE-Based Noise Power Estimation With Low Complexity and Low Tracking Delay

By Timo Gerkmann, Richard Christian Hendriks. IEEE Transactions on Audio, Speech, and Language Processing, 2012

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Improving GANs for Speech Enhancement (2020, background): “3025020 speech enhancement performance have been reported by these deep-learning based methods over more conventional ones [9], [10]–[12].”
DeepMMSE: A Deep Learning Approach to MMSE-Based Noise Power Spectral Density Estimation (2020, methodology): “It should be ∗∗ A smoothing factor of 0.98 is used for the MS, MCRA-2, MMSE, SPP methods, as in [14], [17], [20], [22].”
A Hybrid DSP/Deep Learning Approach to Real-Time Full-Band Speech Enhancement (2017, background): “For example, the crude initial noise estimators and the spectral estimators based on spectral subtraction [1] have been replaced by more accurate noise estimators [2], [3] and spectral amplitude estimators [4].”
rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection Method (2019, methodology): “ethod is applicable for the second pass denoising. Three spectral subtraction based speech enhancement methods are considered in this work, and they rely on dierent noise estimation approaches: MMSE [51], MSNE [48], and a modied version of MSNE (MSNE-mod). The unbiased noise power estimation in the ”
Fullsubnet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement (2020, background): “The temporal evolution of frequency-wise STFT magnitude reﬂects the stationarity, which is the foundation for the conventional noise power estimators [12, 13] and speech enhancement methods [14, 15].”