Adaptive noise reduction in aircraft communication systems

Darlington, Paul; Wheeler, P. D.; Powell, Gavin

doi:10.1109/icassp.1985.1168525

Cited by 10 publications

(3 citation statements)

References 2 publications

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“…The LMS algorithm, developed by Hoff in 1959 ͑Widrow et al, 1985;Haykin et al, 1986͒, has been studied in great detail and has found a myriad of applications ͑Widrow et al, 1975;Darlington et al, 1985;Rodriguez et al, 1987;Harrison et al, 1986;Pulsipher et al, 1979;Elliott et al, 1987;Poole et al, 1984͒. Figure 2͑a͒ depicts the basic construct for noise reduction in a linear system with additive noise.…”

Section: A Least-mean-squares "Lms… Algorithmmentioning

confidence: 98%

An adaptive noise reduction stethoscope for auscultation in high noise environments

Patel

Callahan²,

Callahan³

et al. 1998

The Journal of the Acoustical Society of America

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Auscultation of lung sounds in patient transport vehicles such as an ambulance or aircraft is unachievable because of high ambient noise levels. Aircraft noise levels of 90-100 dB SPL are common, while lung sounds have been measured in the 22-30 dB SPL range in free space and 65-70 dB SPL within a stethoscope coupler. Also, the bandwidth of lung sounds and vehicle noise typically has significant overlap, limiting the utility of traditional band-pass filtering. In this study, a passively shielded stethoscope coupler that contains one microphone to measure the (noise-corrupted) lung sound and another to measure the ambient noise was constructed. Lung sound measurements were made on a healthy subject in a simulated USAF C-130 aircraft environment within an acoustic chamber at noise levels ranging from 80 to 100 dB SPL. Adaptive filtering schemes using a least-mean-squares (LMS) and a normalized least-mean-squares (NLMS) approach were employed to extract the lung sounds from the noise-corrupted signal. Approximately 15 dB of noise reduction over the 100-600 Hz frequency range was achieved with the LMS algorithm, with the more complex NLMS algorithm providing faster convergence and up to 5 dB of additional noise reduction. These findings indicate that a combination of active and passive noise reduction can be used to measure lung sounds in high noise environments.

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Section: A Least-mean-squares "Lms… Algorithmmentioning

confidence: 98%

An adaptive noise reduction stethoscope for auscultation in high noise environments

Patel

Callahan²,

Callahan³

et al. 1998

The Journal of the Acoustical Society of America

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“…The correlation between the noise present in the reference channel and the primary input is important: the higher it is, the better the cancellation [13]. A diagram of the concept is pictured in Fig.…”

Section: Theorymentioning

confidence: 99%

“…This was circumvented by adapting the filter used for cancellation only during periods of background noise. A more realistic evaluation [13] of the cockpit environment was performed by simulating a diffuse background noise field and it was concluded that good cancellation was not possible at frequencies above 2 kHz. The study identified coherence between the reference and primary channels as the most important factor in successful cancellation.…”

Section: Introductionmentioning

confidence: 99%

A Background Noise Reduction Technique using Adaptive Noise Cancellation for Microphone Arrays

Spalt

Fuller

Brooks

et al. 2011

17th AIAA/CEAS Aeroacoustics Conference (32nd AIAA Aeroacoustics Conference)

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Background noise in wind tunnel environments poses a challenge to acoustic measurements due to possible low or negative Signal to Noise Ratios (SNRs) present in the testing environment. This paper overviews the application of time domain Adaptive Noise Cancellation (ANC) to microphone array signals with an intended application of background noise reduction in wind tunnels. An experiment was conducted to simulate background noise from a wind tunnel circuit measured by an out-of-flow microphone array in the tunnel test section. A reference microphone was used to acquire a background noise signal which interfered with the desired primary noise source signal at the array. The technique's efficacy was investigated using frequency spectra from the array microphones, array beamforming of the point source region, and subsequent deconvolution using the Deconvolution Approach for the Mapping of Acoustic Sources (DAMAS) algorithm. Comparisons were made with the conventional techniques for improving SNR of spectral and Cross-Spectral Matrix subtraction. The method was seen to recover the primary signal level in SNRs as low as -29 dB and outperform the conventional methods. A second processing approach using the center array microphone as the noise reference was investigated for more general applicability of the ANC technique. It outperformed the conventional methods at the -29 dB SNR but yielded less accurate results when coherence over the array dropped. This approach could possibly improve conventional testing methodology but must be investigated further under more realistic testing conditions. = data window weighting constant x n = reference input at step n y n = FIR filter output at step n z n = ANC output at step n = coherence ε n = ANC error at step n μ = LMS algorithm step size τ = time delay τ m = propagation time from grid point to microphone m

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Digital Stethoscopes

Sen‐Gupta

Ng²

2010

Practical Signal and Image Processing in Clinical Cardiology

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Adaptive noise reduction in aircraft communication systems

Cited by 10 publications

References 2 publications

An adaptive noise reduction stethoscope for auscultation in high noise environments

An adaptive noise reduction stethoscope for auscultation in high noise environments

A Background Noise Reduction Technique using Adaptive Noise Cancellation for Microphone Arrays

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