Hümmer C, Maas R, Kellermann W (2015)
Publication Language: English
Publication Status: Published
Publication Type: Journal article, Original article
Publication year: 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Book Volume: 22
Pages Range: 1874-1878
Article Number: 7115075
Journal Issue: 11
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.
APA:
Hümmer, C., Maas, R., & Kellermann, W. (2015). The NLMS algorithm with time-variant optimum stepsize derived from a Bayesian network perspective. IEEE Signal Processing Letters, 22(11), 1874-1878. https://dx.doi.org/10.1109/LSP.2015.2439392
MLA:
Hümmer, Christian, Roland Maas, and Walter Kellermann. "The NLMS algorithm with time-variant optimum stepsize derived from a Bayesian network perspective." IEEE Signal Processing Letters 22.11 (2015): 1874-1878.
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