The NLMS algorithm with time-variant optimum stepsize derived from a Bayesian network perspective
This work addresses acoustic echo cancellation, a domain-specific problem in signal processing, with an incremental improvement by reformulating an existing optimal stepsize method using Bayesian networks.
The authors tackled the problem of linear acoustic echo cancellation by deriving a new stepsize adaptation for the normalized least mean square algorithm from a Bayesian network perspective, resulting in the EM-NLMS algorithm that experimentally verified performance in synthesized scenarios with white noise and male speech inputs.
In this article, 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.