A low distortion adaptive noise cancellation structure for real time applications

Al-Kindi, Manal J.; Dunlop, J.

doi:10.1109/icassp.1987.1169423

Cited by 18 publications

(2 citation statements)

References 3 publications

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“…These applications include long distance telephone calls, adaptive antenna array processing, and adaptive line enhancement. Many two-microphone ANCs have been proposed in the literature of adaptive filtering using least mean-square (LMS)-based algorithms that alter the step-size of the update equation to improve the tracking ability of the algorithm and its speed of convergence as well [1][2][3][4][5] .…”

Section: Introductionmentioning

confidence: 99%

A Three-Microphone Adaptive Noise Canceller for Minimizing Reverberation and Signal Distortion

Ramadan¹

2008

American J. of Applied Sciences

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This paper introduces an adaptive noise canceller (ANC) to improve the system performance in the presence of signal leakage components. The proposed ANC consists of two adaptive filters and three microphones. The first adaptive filter cancels the signal leakage and the second filter cancels the noise. For best results, a least mean squares adaptive algorithm was also introduced and used in the proposed ANC. In this algorithm the step size was based on both error vector and data normalization. Simulation results, carried out using a real speech, demonstrate significant improvements of the proposed ANC over the conventional one in minimizing signal distortion and reverberation.

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Section: Introductionmentioning

confidence: 99%

A Three-Microphone Adaptive Noise Canceller for Minimizing Reverberation and Signal Distortion

Ramadan¹

2008

American J. of Applied Sciences

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“…White Gaussian noise of zero mean and unit variance is mixed with the signals after passing through a second order low-pass butterworth filter with an 8 dF3 attenuation in gain. These multi-tone and path approximate one of real models in diver voice communications [6], [7]. The number of taps of the filter N is set 12.…”

Section: B Convergence Characteristicsmentioning

confidence: 99%

A fast convergence algorithm for adaptive FIR filters

Sugiyama

Swamy

Plotkin

International Conference on Acoustics, Speech, and Signal Processing

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This paper presents a fast convergence algorithm for adaptive FIR filters which is a family of the LMS Algorithm.The proposed algorithm controls the step size on the basis of moving average of misadjustment level for a faster convergence. Several additional operations to the LMS operations estimate the misadjustment in the existence of an interference to the misadjustment and average it to provide a stable step size. The new algorithm is applied to adaptive noise cancellation to observe the performance. Computer simulation results showed superior convergence characteristics of the proposed algorithm, improving as much as 90 % over the LMS Algorithm for a multi-tone signal. For a real speech signal, improvent of the new algorithm is degraded but still mom than 50 96 of the LMS convergence time is saved in the simulated case. The acceleration technique of the proposed algorithm is general, thus, could be applied to other algorithms and a variety of applications.

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Single-Microphone Noise Suppression for 3G Handsets Based on Weighted Noise Estimation

Sugiyama

Kato

Serizawa

Signals and Communication Technology

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A low distortion adaptive noise cancellation structure for real time applications

Cited by 18 publications

References 3 publications

A Three-Microphone Adaptive Noise Canceller for Minimizing Reverberation and Signal Distortion

A Three-Microphone Adaptive Noise Canceller for Minimizing Reverberation and Signal Distortion

A fast convergence algorithm for adaptive FIR filters

Single-Microphone Noise Suppression for 3G Handsets Based on Weighted Noise Estimation

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