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Speech enhancement in discontinuous transmission systems using the constrained-stability least-mean-squares
J M Górriz1, J Ramírez, S Cruces-Alvarez
1Department of Signal Theory, University of Granada, Andalucia, Spain. gorriz@ugr.es
The Journal of the Acoustical Society of America
|February 12, 2009
Summary
A new constrained-stability least-mean-squares (LMS) algorithm enhances adaptive noise cancellation (ANC) for speech. This novel method ensures stability and improves performance in systems with discontinuous speech transmission.
Area of Science:
- Signal Processing
- Adaptive Filtering
- Speech Enhancement
Background:
- Adaptive noise cancellation (ANC) is crucial for improving speech quality in noisy environments.
- Traditional least-mean-squares (LMS) algorithms face challenges with stability and performance in systems with discontinuous signals.
- Speech transmission systems often exhibit rapid signal transitions, demanding robust noise cancellation techniques.
Purpose of the Study:
- To introduce a novel constrained-stability least-mean-squares (LMS) algorithm for adaptive noise cancellation (ANC).
- To ensure algorithm stability by minimizing weight vector changes under a posteriori estimation error constraint.
- To analyze the convergence and stability performance of the proposed algorithm.
Main Methods:
- Utilized Lagrangian methodology to develop a nonlinear adaptation based on differential input and error.
- Investigated convergence by analyzing the evolution of natural modes towards the Wiener-Hopf solution.
- Evaluated stability based on the adaptation parameter (mu) and eigenvalues of the difference matrix (DeltaR(1)).
Main Results:
- The proposed constrained-stability LMS algorithm demonstrates superior performance in ANC for discontinuous speech transmission.
- Achieved enhanced stability, with performance solely dependent on the adaptation parameter and specific matrix eigenvalues.
- Experimental analysis on AURORA 3 speech databases confirmed superior performance compared to standard and modified LMS variants.
Conclusions:
- The novel constrained-stability LMS algorithm offers improved performance and guaranteed stability for ANC in challenging speech environments.
- The algorithm's stability is predictable and controllable through the adaptation parameter and system matrix properties.
- This advancement provides a more robust solution for noise reduction in speech discontinuous transmission systems.
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