Single-sensor active noise cancellation

“…Paliwal and Basu have used an estimation of speech signal parameters for clean speech, before it gets disturbed by white noise [6]. A time-adaptive algorithm to adaptively estimate the speech model parameters and noise variance is used by Oppenheim et al [7]. Expectation-Maximization algorithm iteratively estimate the Spectral parameters of speech and noise parameter have proposed by Gannot [8].…”

Section: Introductionmentioning

confidence: 99%

Speech Enhancement Using Iterative Kalman Filter with Time and Frequency Mask in Different Noisy Environment

Rao¹,

Kumar²

2016

IJSIP

View full text Add to dashboard Cite

show abstract

“…T HE AR modeling technique has been successfully used in wide range of applications such as speech analysis [1], [2], spectrum estimation [3], [4], and noise cancellation [5]. Given a random signal, the main task of this technique is to find the optimal AR coefficients that minimize a meansquare error (MSE) criterion.…”

Section: Introductionmentioning

confidence: 99%

Adaptive AR modeling in white Gaussian noise

Chen²

1997

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

Abstract-Autoregressive (AR) modeling is widely used in signal processing. The coefficients of an AR model can be easily obtained with a least mean square (LMS) prediction error filter. However, it is known that this filter gives a biased solution when the input signal is corrupted by white Gaussian noise. Treichler suggested the -LMS algorithm to remedy this problem and proved that the mean weight vector can converge to the Wiener solution. In this paper, we develop a new algorithm that extends works of Vijayan et al. for adaptive AR modeling in the presence of white Gaussian noise. By theoretical analysis, we show that the performance of the new algorithm is superior to the -LMS filter. Simulations are also provided to support our theoretical results.

show abstract

Single-sensor active noise cancellation

Cited by 69 publications

References 10 publications

DSPs/FPGAs Comparative Study for Power Consumption, Noise Cancellation, and Real Time High Speed Applications

DSPs/FPGAs Comparative Study for Power Consumption, Noise Cancellation, and Real Time High Speed Applications

Speech Enhancement Using Iterative Kalman Filter with Time and Frequency Mask in Different Noisy Environment

Adaptive AR modeling in white Gaussian noise

Contact Info

Product

Resources

About