An Efficient Adaptive Noise Cancellation Scheme Using ALE and NLMS Filters

The basic theme of our paper is to implement a new idea of noise reduction in the real time applications using the concepts of adaptive filters.  Our model which is presented as one of the solutions is based on two stages of operation with the first stage based on the ALE (Adaptive Line Enhancer) filters and the second stage on NLMS (Normalized Least Mean Square) filter. The first stage reduces the sinusoidal noise from the input signal and the second stage reduces the wideband noise. Two input sources of voice are used; one for the normal speech and the other for the noise input, using separate microphones for both signals. The first signal is of the corrupted speech signal and the second signal is of only the noise containing both wideband and narrowband noise. In the first stage the narrowband noise is reduced by using the ALE technique. The second stage gets a signal with ideally only the wideband noise which is reduced using the NLMS technique.  In both the stages the concerned algorithms are used to update the filter coefficients in such a way that the noise is cancelled out from the signal and a clean speech signal is heard at the output.DOI:http://dx.doi.org/10.11591/ijece.v2i3.24

Implementasi Sistem Penghilang Derau Adaptif Menggunakan Algoritma LMS pada FPGA Altera Flex10KLC84

oai:jurnal.ugm.ac.id:article/1914The Adaptive Noise Cancelling Systemhas been implemented in FPGA’s AlteraFelx10KCL84, chip which has 576 LE and 3 EAB. Thesystem has data communication capability betweenPC and system and the baudrate is 9,600bps. Dataformat using 8-bit data width two’s complementinteger and 8 scale factor. Total resources which hasbeen used is 564 LE and 2 EAB, after optimization.The accuracy is 100% according to the MATLABresults for the same computation equation.Systemrespond for square wave is better then sinusoidalwave

Speech Enhancement Based on Adaptive Noise Cancellation and Particle Swarm Optimization

Speech enhancement is used in almost all modern communication systems. This is due to the quality of speech being degraded by environmental interference factors, such as: Acoustic additive noise, acoustic reverberation or white Gaussian noise. This paper, explores the potential of different benchmark optimization techniques for enhancing the speech signal. This is accomplished by fine tuning filter coefficients using a diverse set of adaptive filters for noise suppression in speech signals. We consider the Particle Swarm Optimization (PSO) and its variants in conjunction with the Adaptive Noise Cancellation (ANC) approach, for delivering dual speech enhancement. Comparative simulation results demonstrate the potential of an optimized coefficient ANC over a fixed one. Experiments are performed at different signal to noise ratios (SNRs), using two benchmark datasets: the NOIZEUS and Arabic dataset. The performance of the proposed algorithms is evaluated by maximising the perceptual evaluation of speech quality (PESQ) and comparing to the audio-only Wiener Filter (AW) and the Adaptive PSO for dual channel (APSOforDual) algorithms