Measurement, analysis and simulation of wind noise signals for mobile communication devices

Nelke, Christoph Matthias; Vary, Peter

doi:10.1109/iwaenc.2014.6954312

Cited by 17 publications

(8 citation statements)

References 16 publications

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“…The resulting fast changes in the signal level are responsible for the poor performance of conventional noise estimation algorithms which assume a more stationary noise signal compared to the speech signal. In [3]…”

Section: Signal Statisticsmentioning

confidence: 99%

“…These algorithms rely on the assumption that the desired speech signal can be distinguished from the unwanted noise by its temporal characteristics in terms of lower variation of the noise PSD over time. This is, however, not true for wind noise, that is characterized by a high level of non-stationarity (e.g., [3]). For this reason special algorithms were developed for the detection and estimation of wind noise using a single microphone [4], [5], [6].…”

Section: Introductionmentioning

confidence: 98%

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Corpus based reconstruction of speech degraded by wind noise

Nelke¹,

Naylor²,

Vary³

2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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This contribution addresses the problem of enhancing a speech signal which is degraded by wind noise. The characteristic that wind noise signals are sparse in time and frequency is exploited in a way that only time-frequency regions that are determined as degraded are enhanced. In these regions of the noisy signal, a process is applied to reconstruct the clean speech data. This is realized by a separation of the noisy speech signal into an autoregressive filter representing the human vocal tract and its excitation signal. The clean filter coefficients of the former are estimated using a pre-trained codebook. A pitch cycle taken from clean speech is adapted to reconstruct the excitation of noisy speech segments.

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Section: Signal Statisticsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Corpus based reconstruction of speech degraded by wind noise

Nelke¹,

Naylor²,

Vary³

2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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show abstract

“…The mechanism and power spectrum of outdoor wind noise have been deeply studied in many works [ 21 , 22 , 23 , 24 , 25 ]. Morgan and Raspet have concluded, “The dominant source of wind noise in outdoor microphones is the pressure fluctuations that are caused by the velocity fluctuations of the incoming flow”.…”

Section: Introductionmentioning

confidence: 99%

“…From [ 23 ], wind noise changes the characteristics of the low-frequency bands of the detected acoustic signal quickly and temporally. It is difficult to separate the target acoustic signal and the wind noise from a single channel acoustic signal, both in the time domain and the frequency domain.…”

Section: Introductionmentioning

confidence: 99%

A Robust Real Time Direction-of-Arrival Estimation Method for Sequential Movement Events of Vehicles

Liu

Zhou

et al. 2018

Sensors

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Parameters estimation of sequential movement events of vehicles is facing the challenges of noise interferences and the demands of portable implementation. In this paper, we propose a robust direction-of-arrival (DOA) estimation method for the sequential movement events of vehicles based on a small Micro-Electro-Mechanical System (MEMS) microphone array system. Inspired by the incoherent signal-subspace method (ISM), the method that is proposed in this work employs multiple sub-bands, which are selected from the wideband signals with high magnitude-squared coherence to track moving vehicles in the presence of wind noise. The field test results demonstrate that the proposed method has a better performance in emulating the DOA of a moving vehicle even in the case of severe wind interference than the narrowband multiple signal classification (MUSIC) method, the sub-band DOA estimation method, and the classical two-sided correlation transformation (TCT) method.

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“…This makes the reduction of wind noise a challenging task. The suppression of wind noise is mostly covered in the context of digital hearing aids or mobile devices in the literature [17,20,21]. For single-channel wind noise reduction, often the different power spectral density (PSD) properties of speech and wind noise are exploited [17,20,22].…”

Section: Introductionmentioning

confidence: 99%

Wind noise reduction for a closely spaced microphone array in a car environment

Grimm¹,

Freudenberger²

2018

J AUDIO SPEECH MUSIC PROC.

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This work studies a wind noise reduction approach for communication applications in a car environment. An endfire array consisting of two microphones is considered as a substitute for an ordinary cardioid microphone capsule of the same size. Using the decomposition of the multichannel Wiener filter (MWF), a suitable beamformer and a single-channel post filter are derived. Due to the known array geometry and the location of the speech source, assumptions about the signal properties can be made to simplify the MWF beamformer and to estimate the speech and noise power spectral densities required for the post filter. Even for closely spaced microphones, the different signal properties at the microphones can be exploited to achieve a significant reduction of wind noise. The proposed beamformer approach results in an improved speech signal regarding the signal-to-noise-ratio and keeps the linear speech distortion low. The derived post filter shows equal performance compared to known approaches but reduces the effort for noise estimation.

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Measurement, analysis and simulation of wind noise signals for mobile communication devices

Cited by 17 publications

References 16 publications

Corpus based reconstruction of speech degraded by wind noise

Corpus based reconstruction of speech degraded by wind noise

A Robust Real Time Direction-of-Arrival Estimation Method for Sequential Movement Events of Vehicles

Wind noise reduction for a closely spaced microphone array in a car environment

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