In this paper, the application of the minimum mean square error criterion for the feedback cancellation problem in a speech reinforcement system for cars has been studied. The solution obtained for a causal FIR filter has been derived and compared to the solution for an unconstrained identification filter. The use of adaptive whitening filters has been proposed and justified for reducing the error in the identification of the Loudspeaker-Enclosure-Microphone (LEM) path. This identifcation mismatch can be reduced by modifying the cost function. This modification consists of the filtering of the error signal before minimizing its variance using a whitening filter. The use of the modified cost function leads to the use of the FX-LMS algorithm in its adaptive implementation. Simulation results confirm the theoretical study presented here. The comparison between the weight misadjustment obtained with and without adaptive linear prediction shows that the minimization of the mean squared whitened error produces more accurate estimates of the LEM path than the use of the classical LMS algorithm for speech reinforcement systems for vehicles.
Acoustic feedback cancellation in speech reinforcement systems for vehicles
2005
4 Seiten, 6 Bilder, 5 Quellen
Aufsatz (Konferenz)
Englisch
Adaptivfilter , akustische Rückkopplung , Echounterdrückung , elektroakustische Eigenschaft , Fahrzeuginnenraum , FIR-Filter , Frequenzbereichsanalyse , Implementierung , Kostenfunktion , Lautsprecher , Mikrophon , Regeleigenschaft , simuliertes Ergebnis , Systembeschreibung , Vorhersage , Zeitbereichanalyse
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