Performance of Wiener Filter and Adaptive Filter for Noise Cancellation in Real-Time Environment

Abstract

Form the past several decades' noise cancellation in speech signal gains researchers' attention. Several techniques were developed for noise cancellation among them optimal wiener filter can be the one of the most fundamental approach for noise cancellation. Later on adaptive filter was introduced to attain better performance. This paper shows the capacity of wiener filter and adaptive filter for removal of noise by estimating the signal by means of removing the noise signal form the corrupted signal.Wiener filter plays a central role in wide range of applications such as linear prediction, echo cancellation, signal restoration, channel equalization and system identification. In this paper the performance of wiener filter and adaptive filter for removal of noise in the presence of real time environment are compared. In the existing papers the authors have proposed the theory of wiener filter and adaptive filter algorithms in real time environment like recorded speech. So this is paper is going to take the part of the existing paper and going to perform the noise cancellation. In order to measure the performance step size is the main factor for the convergence speed and mean square error. Wiener filter provides better performance for noise cancellation but it requires large no. of computations i.e., complexity and cost of the system is going to increase, so adaptive filter is the alternate approach for removal of noise with moderate complexity and cost. The simulation result clearly shows that wiener filter gives the better performance but due to high cost adaptive filter is the choice of many applications. This paper is going to discuss about wiener filter theory, wiener filter problem, solution to optimal filtering, adaptive filtering, adaptive algorithm, study of wiener filter and adaptive filter for noise reduction etc.

Analysis of the Wiener filters application to the spectral fluctuation patterns segmentation

“…Yadav dkk. [16] mengusulkan metode peningkatan tapis Wiener dan filter subspace untuk pengurangan derau pada teknologi subspace speech yang menghasilkan kinerja yang lebih baik dalam menghilangkan derau.…”

“…MSE digunakan untuk menghitung perbandingan antara dua buah sinyal atau menghitung tingkat kesamaan dua buah sinyal (Persamaan 4) [16]. Nilai n menyatakan jumlah segmen, I adalah sinyal acuan, dan K adalah sinyal estimasi.…”

“…Hal tersebut menjelaskan bahwa dengan meminimalkan nilai MSE mampu memberikan hasil yang relatif lebih konsisten dan menghasilkan rata-rata kesalahan yang kecil. Hal tersebut sesuai dengan yang ditunjukkan pada [16] dalam Tabel 2 yang merupakan hasil peringkat lima segmen yang mempunyai kualitas sinyal terbaik di seluruh segmen pada pola fluktuasi HF. Nilai terbaik SNR dan MSE terdapat pada segmen 28 dengan sinyal masukan 700 kHz, dimana SNR bernilai 10,7382 dB dan MSE bernilai 0,7753, sedangkan peringkat ke-5 terdapat pada segmen 29 dengan sinyal masukan 800 kHz, dimana SNR bernilai 2,3485 dB dan MSE bernilai 0,5639.…”

“…Hal tersebut ditunjukkan dengan nilai daya amplitudo yang lebih rendah bila dibandingkan dengan nilai daya amplitudo sebelum ditapis. Hal ini sejalan dengan [11], [16] yang menyatakan bahwa tapis Wiener telah didesain sedemikian rupa sehingga keluaran dari tapis dapat meminimalkan pengaruh derau dari data awal sebagai masukan tapis.…”

Performance Comparison of Different Variable Filters for Noise Cancellation in Real-Time Environment

“…The algorithms are selected depending upon the requirement of the user [1]. Wiener filter approach is the one of the oldest method used for noise cancellation and replaced because of its high degree of complexity and low speed [2][3]. In the existing work authors proposed the adaptive filter algorithms like LMS, NLMS and RLS for noise cancellation and their performance is compared, but they have taken the input in sinusoidal environment.…”