Efficient environmental noise reduction system for speech enhancement via fuzzy-based extreme learning machine and fuzzy adaptive Wiener filter

This paper proposes the application of fuzzy-based extreme learning machine (FELM) 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 minimizes the mean square error and it provides better performance than the conventional filters. Though the background noise is uncertain, fuzzy inference systems (FIS) are proposed in ELM to classify the background noises and Wiener filters are proposed to update Wiener filter coefficients that will increase the SNR of the filtered speech signal. Fuzzy adaptive Wiener filter depends on the adaptation of the filter transfer function from sample to sample based on the signal statistics and it is implemented in time domain rather than in frequency domain to accommodate for the varying nature of the speech signal. The proposed FELM is compared with ELM and fuzzy radial basis function network (FRBFN) for noise classification and fuzzy adaptive Wiener filter is compared with Wiener and adaptive Wiener filters for noise cancellation. Simulation results show that FELM improves the percentage of classification by 9% than FRBFN, 4% than ELM and fuzzy adaptive Wiener filter improves the SNR by 8 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 5 dB.