Abstract In this chapter, we present a modified cerebellar model articulation controller (MCMAC) to be used together with the amplitude spectral estimator (ASE) for enhancing noisy speech. The MCMAC training overcomes the limitations of the CMAC technique we have employed noise/echo cancellation in a vehicular environment. While the CMAC in the training mode has trained only the trajectory it has visited by controlling the reference input, the modified MCMAC-ASE system architecture proposed in this work includes multiple MCMAC memory trainable for different noise sources.
Modified Cerebellar Model Articulation Controller (MCMAC) as an Amplitude Spectral Estimator for Speech Enhancement
DSP for In-Vehicle and Mobile Systems ; 123-137
01.01.2005
15 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
in-car noise , Cerebellar model articulation controller (CMAC) , Kohonen’s self-organizing neural network (SFON) , neighborhood function , and MOS , echo cancellation , Grossberg learning rule , speech enhancement , Wiener filtering , amplitude spectral estimation Signal, Image and Speech Processing , Communications Engineering, Networks , Engineering , User Interfaces and Human Computer Interaction
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