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A Unified Framework for Designing Optimal STSA Estimators Assuming Maximum

Summary: 1
A Unified Framework for Designing Optimal
STSA Estimators Assuming Maximum
Likelihood Phase Equivalence of Speech and
Bengt J. Borgstršom, Member, IEEE, and Abeer Alwan, IEEE Fellow
In this paper, we present a stochastic framework for designing optimal short-time spectral amplitude
(STSA) estimators for speech enhancement assuming phase equivalence of speech and noise. By
assuming additive superposition of speech and noise, which is implied by the maximum likelihood (ML)
phase estimate [6], we effectively project the optimal spectral amplitude estimation problem onto a 1-
dimensional subspace of the complex spectral plane, thus simplifying the problem formulation. Assuming
generalized Gamma distributions (GGDs) for a priori distributions of both speech and noise STSAs,
we derive separate families of novel estimators according to either the maximum likelihood (ML), the
minimum mean-square error (MMSE), or the maximum a posteriori (MAP) criterion. The use of GGDs
allows optimal estimators to be determined in a generalized form, so that particular solutions can be
obtained by substituting statistical shape parameters corresponding to expected speech and noise priors.
It is interesting to note that several of the proposed estimators exhibit strong similarities to well-known
STSA solutions. For example, the magnitude spectral subtracter (MSS) [2] and Wiener filter (WF) [1]
are obtained for specific cases of GGD shape parameters. Quantitative analysis of a selected subset of the


Source: Alwan, Abeer - Electrical Engineering Department, University of California at Los Angeles


Collections: Computer Technologies and Information Sciences