Environmental noise reduction system using fuzzy neural network and adaptive fuzzy algorithms

This article proposes the application of fuzzy based radial basis function network (FRBFN) and fuzzy based adaptive Wiener filter for background noise reduction system to improve the signal-to-noise ratio (SNR) and to reduce the minimum mean square error (MMSE). The Wiener filter works as to minimise the mean square error, and it provides better performance than the conventional filters. Though the background noise is uncertain, fuzzy inference systems are proposed in RBFN to classify the background noises and in Wiener filter to update Wiener filter coefficients that will increase the SNR of the filtered speech signal. The proposed FRBFN is compared with RBFN for noise classification, and fuzzy adaptive Wiener filter is compared with Wiener and adaptive Wiener filters for noise cancellation. Simulation result shows that FRBFN improves the percentage of classification by 7% than RBFN, and fuzzy adaptive Wiener filter improves the SNR by 6?dB than the conventional Wiener filter. The real time implementation of the system is done using TMS320C6713 DSK starter kit. The real time practical setup using DSK shows an improved SNR of 4?dB.