Abstract
This paper presents the new approach to introducing adaptive Filter with LMS Algorithm based on Shadow concept. Which is useful for the cancellation of the noise component overlap with Speech signal in the same frequency range, but fixed LMS algorithm produces minimum convergence rate and fixed steady state error. So we presents design, implementation and performance of adaptive FIR filter, based on Shadow concept, which produces minimum mean square error compare to fixed LMS, and we also obtains denoised Speech signal at output, and also we propose to calculate SNR values of Adaptive Filter with LMS algorithm with and without Shadow concept.
Highlights
Calculate input SNR and output SNR4. Design of Adaptive Filter with Fixed LMS Algorithm based on Shadow Concept The Figure 2 shows the block diagram of Adaptive filter with Fixed LMS Algorithm with
Where s(n) = clean speech signal v(n) = noise signal h = Low pass finite impulse response (FIR) Filter v1(n) = h * v(n) d(n) = noised speech signal, [s(n)+v1(n)] y(n) = Filtered Noise signal e(n) = d(n)-y(n), [Original speech signal] the adjustable weights are typically determined by the LMS Algorithm, the weight update equation is w(n+1) = w(n)+μ*e(n)*v1(n) y(n) = w(n)+e(n)*v1(n)
Design of Adaptive Filter with Fixed LMS Algorithm based on Shadow Concept The Figure 2 shows the block diagram of Adaptive filter with Fixed LMS Algorithm with
Summary
4. Design of Adaptive Filter with Fixed LMS Algorithm based on Shadow Concept The Figure 2 shows the block diagram of Adaptive filter with Fixed LMS Algorithm with. Shadow concept In shadow filter mechanism the Low pass filter output is feedback either positively or negatively by a shadow filter of same type or different type. We used the shadow mechanism to find best combination for different values of ‘β’. We can derive expression of the transfer function for the shadow mechanism with positive feedback connection is,
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