Suppression of acoustic noise in speech using two microphone adaptive noise cancellation

Boll, S.; Pulsipher, D. C.

doi:10.1109/tassp.1980.1163472

Cited by 92 publications

(31 citation statements)

References 5 publications

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“…These results demonstrate the importance of including the effects of algorithm noise in analyzing the performance of real-time adaptive processors. bioengineering 11] , echo cancellation [2,3], data equalization 14-6], predictive deconvolution [7], instantaneous frequency tracking 18-101, speech processing 11, [11][12][13] [25,31,32], and narrowband interference rejection 1331.…”

Section: Discussionmentioning

confidence: 99%

Adaptive enhancement of finite bandwidth signals in white Gaussian noise

Anderson

Satorius

Zeidler³

1983

IEEE Trans. Acoust., Speech, Signal Process.

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Section: Discussionmentioning

confidence: 99%

Adaptive enhancement of finite bandwidth signals in white Gaussian noise

Anderson

Satorius

Zeidler³

1983

IEEE Trans. Acoust., Speech, Signal Process.

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“…In addition, the average power of the total broad-band noise at the victim antenna is given by (18) where is the power spectral density of . Therefore, the SNR before and after cancellation may be expressed as (19) Here, the powers are measured at a time , the numerators are constant, and the frequency domain representation of is obtained from (17).…”

Section: A Cancellation Approachmentioning

confidence: 99%

“…Furthermore, band limiting through the emulation filter helps prevent the coupled noise from decorrelating for a larger time interval, allowing easier cancellation, as suggested in [17]. Besides, as noted in [18], some applications may necessitate the use of noncausal filters in the adaptation path. Deriving the correlated input directly from the aggressor, however, negates this requirement since the interferer does not reach the victim before it does the cancellation unit.…”

Section: B Proposed Canceller Unitmentioning

confidence: 99%

Analysis and design of an interference canceller for collocated radios

Raghavan

Gebara

Tentzeris

et al. 2005

IEEE Trans. Microwave Theory Techn.

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Abstract-An active interference cancellation scheme is presented to mitigate interference between Bluetooth and wireless local area network (IEEE 802.11 b) radios operating in close proximity. This method is extensible to other mutually interfering radio devices. A reference signal correlated to the original interferer is used to generate a cancellation signal by means of amplitude and phase alignment, and filtration. The filter employed emulates the coupling channel responsible for interference. An implementation of this procedure in 0.18-m Si-complementary metal-oxide-semiconductor (CMOS) integrated-circuit (IC) technology is also presented. The circuits fabricated are tunable and are controlled by a closed-loop adaptive process including an error minimization method. The cancellation system designed achieves 15-30 dB of interference suppression for different cases. A total power of 14 mW is dissipated by the CMOS ICs designed.

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“…An early, simplified experiment employing ANC reported at least 20 dB noise cancellation with minimum distortion [11]. Background noise in a fighter jet cockpit presented an important application of the technique that led to the identification of the major challenges for successful implementation.…”

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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Suppression of acoustic noise in speech using two microphone adaptive noise cancellation

Cited by 92 publications

References 5 publications

Adaptive enhancement of finite bandwidth signals in white Gaussian noise

Adaptive enhancement of finite bandwidth signals in white Gaussian noise

Analysis and design of an interference canceller for collocated radios

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

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