Linking speech enhancement and error concealment based on recursive MMSE estimation
Balázs Fodor, Florian Pflug, Tim Fingscheidt · EURASIP Journal on Advances in Signal Processing · 2015
Speech enhancement and error concealment have seen a considerable progress over the past decades. Although both fields deal with distorted speech signals, there has rarely been an attempt to relate respective approaches to each other. In this paper, for the first time, a clear synopsis of recursive minimum mean square error (MMSE) estimation in both fields is provided. Our work intentionally does not propose a certain algorithm furthering the state of the art, nor does it provide simulation results of algorithms. Instead, our aim is threefold: First we revisit the basics of Bayes estimation in a recursive manner, covering both kinds of distortion acoustic noise as well as transmission channel noise. Second, we present recursive MMSE estimation applied to speech enhancement (in the frequency domain, as typical) and applied to error concealment (in the time domain, as typical) in strictly coherent notations and provide respective overview diagrams. Finally, we discuss commonalities and differences between both approaches, identify a particular strength of error concealment in general, and provide possible research directions for speech enhancement. A particularly interesting observation is that noise introduced by error concealment is far from being Gaussian and that additive acoustic noise can be expressed in terms of bit errors in DFT coefficients providing a potential interface to error concealment approaches.