A Bayesian network viewon linear and nonlinear acoustic echo cancellation

Hofmann

2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

et al. 2014

Self Cite

In this article, we introduce a novel approach for estimating the coefficients of a memoryless preprocessor for nonlinear acoustic echo cancellation (NL-AEC) using particle filtering. The acoustic echo path is modeled by a nonlinearlinear cascade of a memoryless preprocessor (to model the loudspeaker nonlinearities) preceding a linear finite impulse response filter (estimated by the normalized least mean square algorithm). For identifying the loudspeaker signal distortions, we follow the concept of significance-aware filtering by modeling the time-variant coefficients of the memoryless preprocessor and the direct-path part of the room impulse response vector as one state vector with non-Gaussian probability distribution. Due to the nonlinear relation between the state vector and the observation, we propose a computationally-efficient realization of the recently published elitist particle filter based on evolutionary strategies (EPFES), which evaluates realizations of the state vector based on long-term fitness measures. The experimental validation comprises predefined loudspeaker signal distortions as well as real recordings stemming from a commercial smartphone. In comparison to the well-known Hammerstein group model for NL-AEC, the computational complexity is reduced and the achievable system identification is improved for both scenarios.

“…It should be mentioned, that the remaining Lc elements ofẑ n+1 are not used as estimateĥ c,n+1 , as the linear FIR filterĥ n+1 is fully determined by the NLMS estimation in (6).…”

Section: B the Sa-epfes To Estimate The Preprocessor Coefficientsmentioning

confidence: 99%

Section: Estimation Of the Preprocessor Coefficientsmentioning

confidence: 99%

Section: B the Sa-epfes To Estimate The Preprocessor Coefficientsmentioning

confidence: 99%

Section: B the Sa-epfes To Estimate The Preprocessor Coefficientsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The significance-aware EPFES to estimate a memoryless preprocessor for nonlinear acoustic echo cancellation

Hofmann

2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

et al. 2014

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The NLMS Algorithm with Time-Variant Optimum Stepsize Derived from a Bayesian Network Perspective

IEEE Signal Process. Lett.

Kellermann

2015

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In this letter, we derive a new stepsize adaptation for the normalized least mean square algorithm (NLMS) by describing the task of linear acoustic echo cancellation from a Bayesian network perspective. Similar to the well-known Kalman filter equations, we model the acoustic wave propagation from the loudspeaker to the microphone by a latent state vector and define a linear observation equation (to model the relation between the state vector and the observation) as well as a linear process equation (to model the temporal progress of the state vector). Based on additional assumptions on the statistics of the random variables in observation and process equation, we apply the expectation-maximization (EM) algorithm to derive an NLMS-like filter adaptation. By exploiting the conditional independence rules for Bayesian networks, we reveal that the resulting EM-NLMS algorithm has a stepsize update equivalent to the optimal-stepsize calculation proposed by Yamamoto and Kitayama in 1982, which has been adopted in many textbooks. As main difference, the instantaneous stepsize value is estimated in the M step of the EM algorithm (instead of being approximated by artificially extending the acoustic echo path). The EM-NLMS algorithm is experimentally verified for synthesized scenarios with both, white noise and male speech as input signal.Index Terms-Adaptive stepsize, EM algorithm, NLMS.

A Bayesian network approach to linear and nonlinear acoustic echo cancellation

EURASIP J. Adv. Signal Process.

Hofmann

et al. 2015

Self Cite

This article provides a general Bayesian approach to the tasks of linear and nonlinear acoustic echo cancellation (AEC). We introduce a state-space model with latent state vector modeling all relevant information of the unknown system. Based on three cases for defining the state vector (to model a linear or nonlinear echo path) and its mathematical relation to the observation, it is shown that the normalized least mean square algorithm (with fixed and adaptive stepsize), the Hammerstein group model, and a numerical sampling scheme for nonlinear AEC can be derived by applying fundamental techniques for probabilistic graphical models. As a consequence, the major contribution of this Bayesian approach is a unifying graphical-model perspective which may serve as a powerful framework for future work in linear and nonlinear AEC.