Practical adaptive noise reduction in the aircraft cockpit environment

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

doi:10.1109/icassp.1987.1169639

Cited by 7 publications

(2 citation statements)

References 2 publications

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“…al., 1987; Powell et al, 1987) and other military vehicles (Kang and Fransen, 1987). Many of the reported applications of ANC have employed the well-known leastmean squares (LMS) adaptive filter characterized by the update equation (Widrow e!…”

Section: Introductionmentioning

confidence: 99%

The performance of adaptive noise cancellation systems in reverberant rooms

Lu¹,

Clarkson²

1993

The Journal of the Acoustical Society of America

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The performance of adaptive noise cancellation (ANC) systems in a reverberant room is investigated. Room reverberation effects are modeled through the method of images, incorporating both direct signal and reverberation effects into the room impulse response. The performance of ANC for broadband inputs is examined as a function of reverberation time, noise source location, and the geometry of the room and measurement equipment. Expressions are obtained relating the steady-state minimum mean-squared error and excess mean-squared error to the reverberation time and filter parameters. Nonuniform convergence effects are also investigated arid bounds on the eigenvalue disparity are obtained for a given configuration. It is shown that the eigenvalue disparity increases as both the filter length and the distance between noise source and reference receiver increase, approaching a limiting value as the distance from source to reference receiver surpasses the acoustic critical distance.

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Section: Introductionmentioning

confidence: 99%

The performance of adaptive noise cancellation systems in reverberant rooms

Lu¹,

Clarkson²

1993

The Journal of the Acoustical Society of America

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“…If we use two microphones, one to capture the noise signal and the other to pick up the noisy speech signal, we can apply adaptive filtering algorithms, such as least mean squares (LMS), to achieve speech enhancement. This cancels both stationary and nonstationary noise and improves recognition performance in the presence of noise [47,48].…”

Section: Special Transducer Arrangement To Solve the Cocktail Party Problemmentioning

confidence: 99%

Speech‐Recognition Overview

Gold

Morgan

Ellis

2011

Speech and Audio Signal Processing

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Automatic speech recognition is critical in natural human-centric interfaces for ambient intelligence. The performance of an automatic speech recognition system, however, degrades drastically when there is a mismatch between training and testing conditions. The aim of robust speech recognition is to overcome the mismatch problem so the result is a moderate and graceful degradation in recognition performance. In this chapter, we provide a brief overview of an automatic speech recognition system, describe sources of speech variability that cause mismatch between training and testing, and discuss some of the current techniques to achieve robust speech recognition.

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