Wind Noise Reduction Research Articles

Automatic classification and reduction of wind noise in spectral data.

Outdoor acoustic data often include non-acoustic pressures caused by atmospheric turbulence, particularly below a few hundred Hz in frequency, even when using microphone windscreens. This paper describes a method for automatic wind-noise classification and reduction in spectral data without requiring measured wind speeds. The method finds individual frequency bands matching the characteristic decreasing spectral slope of wind noise. Uncontaminated data from several short-timescale spectra can be used to obtain a decontaminated long-timescale spectrum. This method is validated with field-test data and can be applied to large datasets to efficiently find and reduce the negative impact of wind noise contamination.

Identifying suitable time periods for infrasound measurement system response estimation using across-array coherence

SUMMARY Microbarometers deployed to measure atmospheric infrasound are often connected to, or housed within, a wind noise reduction system (WNRS). At infrasound arrays of the International Monitoring System (IMS), being deployed as part of Comprehensive Nuclear-Test-Ban Treaty verification measures, the WNRS typically comprises an 18 m diameter pipe array. Over the past decade an in situ method has been developed to estimate the measurement system (sensor + WNRS) response characteristics, by comparing its recordings with those made on a colocated reference sensor with known response and no WNRS. The method relies upon the identification of time periods for which the reference sensor and measurement system are subject to the same input pressure field. It has proven difficult to reliably identify such time periods at frequencies $\lt 0.1\,$ Hz using recordings at a single location, resulting in a negative bias in estimated measurement system gain values (the ‘dip artefact’) in the 0.02–0.1 Hz passband. The IMS is deploying arrays of microbarometers, and we show that a measure of across-array coherence can be used to identify time periods associated with acoustic signal propagation. Amplitude response estimates, using 1 yr of data from four IMS arrays, indicate that the dip artefact can be removed by retaining for analysis only those time periods that exhibit high across-array coherence. Moreover, our analysis confirms the hypothesis that the dip artefact is associated with time periods during which wind-generated pressure fluctuations dominate, leading to partial suppression of noise with length scales less than the extent of the WNRS. At two arrays within continental forests accurate amplitude responses are estimated across the 0.02–4 Hz passband, as acoustic signals at all frequencies can be identified. At two oceanic island arrays, the low numbers of time windows with above-noise acoustic signal in the 0.02–0.1 Hz passband make reliable response estimation at these frequencies difficult or impossible. It is recommended that the methodology for estimating the response of an infrasound measurement system at an array should incorporate a multichannel coherence measure; data centres may already routinely compute such measures in their signal detection algorithms.

Numerical and Experimental Study on the Shape of a Windscreen for Wind Noise Reduction

Numerical and Experimental Study on the Shape of a Windscreen for Wind Noise Reduction

Examining wind noise reduction effects of windscreens and microphone elevation in outdoor acoustical measurements

Jones et al. [J. Acoust. Soc. Am. 146, 2912 (2019)] compared an elevated (1.5 m) acoustical measurement configuration that used a standard commercial windscreen for outdoor measurements with a ground-based configuration with a custom windscreen. That study showed that the ground-based measurement method yielded superior wind noise rejection, presumably due to the larger windscreen and lower wind speeds experienced near the ground. This study further examines those findings by attempting to decouple the effects of windscreens and microphone elevation using measurements at 1.5 m and near the ground with and without windscreens. Simultaneous wind speed measurements at 1.5 m and near the ground were also made for correlation purposes. Results show that the insertion of the custom windscreen reduces wind noise more than placing the microphone near the ground, and that the ground-based setup is again preferable for obtaining broadband outdoor acoustic measurements.

Classification of Hearing Aids Into Feature Profiles Using Hierarchical Latent Class Analysis Applied to a Large Dataset of Hearing Aids.

We developed a framework for objectively comparing hearing aids, independent of brand, type, or product family. This was done using a large dataset of commercially available hearing aids. To achieve this, we investigated which hearing aid features are suitable for comparison, and are also relevant for the rehabilitation of hearing impairment. To compare hearing aids objectively, we distinguished populations of hearing aids based on a set of key hearing aid features. Finally, we describe these hearing aid subpopulations so that these could potentially be used as a supporting tool for the selection of an appropriate hearing aid. In this study, we used technical (meta-)data from 3911 hearing aids (available on the Dutch market in March 2018). The dataset contained about 50 of the most important characteristics of a hearing aid. After cleaning and handling the data via a well-defined knowledge discovery in database procedure, a total 3083 hearing aids were included. Subsequently, a set of well-defined key hearing aid features were used as input for further analysis. The data were split into an in-the-ear style hearing aid subset and a behind-the-ear style subset, for separate analyses. The knowledge discovery in databases procedure was also used as an objective guiding tool for applying an exploratory cluster analysis to expose subpopulations of hearing aids within the dataset. The latter was done using Latent Class Tree Analysis, which is an extension to the better-known Latent Class Analysis clustering method: with the important addition of a hierarchical structure. A total of 10 hearing aid features were identified as relevant for audiological rehabilitation: compression, sound processing, noise reduction (NR), expansion, wind NR, impulse (noise) reduction, active feedback management, directionality, NR environments, and ear-to-ear communication. These features had the greatest impact on results yielded by the Latent Class Tree cluster analysis. At the first level in the hierarchical cluster model, the two subpopulations of hearing aids could be divided into 3 main branches, mainly distinguishable by the overall availability or technology level of hearing aid features. Higher-level results of the cluster analysis yielded a set of mutually exclusive hearing aid populations, called modalities. In total, nine behind-the-ear and seven in-the-ear modalities were found. These modalities were characterized by particular profiles of (complex) interplay between the selected key features. A technical comparison of features (e.g., implementation) is beyond the scope of this research. Combining a large dataset of hearing aids with a probabilistic hierarchical clustering method enables analysis of hearing aid characteristics which extends beyond product families and manufacturers. Furthermore, this study found that the resulting hearing aid modalities can be thought of as a generic alternative to the manufacturer-dependent proprietary "concepts," and could potentially aid the selection of an appropriate hearing aid for technical rehabilitation. This study is in line with a growing need for justification of hearing aid selection and the increasing demand for evidence-based practice.

Improving Cochlear Implant Performance in the Wind Through Spectral Masking Release: A Multi-microphone and Multichannel Strategy.

Adopting the omnidirectional microphone (OMNI) mode and reducing low-frequency gain are the two most commonly used wind noise reduction strategies in hearing devices. The objective of this study was to compare the effectiveness of these two strategies on cochlear implant users' speech-understanding abilities and perceived sound quality in wind noise. We also examined the effectiveness of a new strategy that adopts the microphone mode with lower wind noise level in each frequency channel. A behind-the-ear digital hearing aid with multiple microphone modes was used to record testing materials for cochlear implant participants. It was adjusted to have linear amplification, flat frequency response when worn on a Knowles Electronic Manikin for Acoustic Research to remove the head-related transfer function of the manikin and to mimic typical microphone characteristics of hearing devices. Recordings of wind noise samples and hearing-in-noise test sentences were made when the hearing aid was programmed to four microphone modes, namely (1) OMNI; (2) adaptive directional microphone (ADM); (3) ADM with low-frequency roll-off; and (4) a combination of omnidirectional and directional microphone (COMBO). Wind noise samples were recorded in an acoustically treated wind tunnel from 0° to 360° in 10° increment at a wind velocity of 4.5, 9.0, and 13.5 m/s when the hearing aid was worn on the manikin. Two wind noise samples recorded at 90° and 300° head angles at the wind velocity of 9.0 m/s were chosen to take advantage of the spectral masking release effects of COMBO. The samples were then mixed with the sentences recorded using identical settings. Cochlear implant participants listened to the speech-in-wind testing materials and they repeated the sentences and compared overall sound quality preferences of different microphone modes using a paired-comparison categorical rating paradigm. The participants also rated their preferences of wind-only samples. COMBO yielded the highest speech recognition scores among the four microphone modes, and it was also preferred the most often, likely due to the reduction of spectral masking. The speech recognition scores generated using ADM with low-frequency roll-off were either equal to or lower than those obtained using ADM because gain reduction decreased not only the level of wind noise but also the low-frequency energy of speech. OMNI consistently yielded speech recognition scores lower than COMBO, and it was often rated as less preferable than other microphone modes, suggesting the conventional strategy to switch to the omnidirectional mode in the wind was undesirable. Neither adopting an OMNI nor reducing low-frequency gain generated higher speech recognition scores or higher sound quality ratings than COMBO. Adopting the microphone with lower wind noise level in different frequency channels can provide spectral masking release, and it is a more effective wind noise reduction strategy. The natural 6 dB/octave low-frequency roll-off of first-order directional microphones should be compensated when speech is present. Signal detection and decision rules for wind noise reduction applications are discussed in hearing devices with and without binaural transmission capability.

Microphone directionality and wind noise reduction enhance speech perception in users of the MED-EL SONNET audio processor

Objectives: Speech understanding in noise remains a challenge for many cochlear implant users. To improve this, the SONNET audio processor features three microphone directionality (MD) settings and three wind noise reduction (WNR) settings. The primary aim of this study was to assess if speech understanding in noise and hearing in real life was superior with the SONNET or with the OPUS 2, which does not feature MD or WNR.Methods: 31 of 33 participants completed the study. Speech understanding was assessed in two types of acoustic noise, in wind noise, and in quiet. A 4-speaker setup was used and speech was presented from 0° and noise from 90°, 180°, and 270°. Wind noise was simulated with a fan. Sound quality and hearing-related abilities were assessed via two subjective questionnaires.Results: Speech understanding in acoustic noise with the SONNET was significantly better or equal to than with the OPUS 2. Speech understanding in wind with the OPUS 2 was significantly better than with the SONNET in some settings. Sound quality and hearing-related abilities were both significantly better with the SONNET.Conclusions: The SONNET provides the same or significantly improved speech understanding than the OPUS 2 in quiet and in noise. While OPUS 2 was superior in wind than the SONNET in some settings, this was offset by SONNET's superiority in real-life listening situations. We therefore conclude that the front-end processing of the SONNET provides users with better hearing than does the OPUS 2.

Efficacy and Effectiveness of Advanced Hearing Aid Directional and Noise Reduction Technologies for Older Adults With Mild to Moderate Hearing Loss.

The purpose of the present study was to investigate the laboratory efficacy and real-world effectiveness of advanced directional microphones (DM) and digital noise reduction (NR) algorithms (i.e., premium DM/NR features) relative to basic-level DM/NR features of contemporary hearing aids (HAs). The study also examined the effect of premium HAs relative to basic HAs and the effect of DM/NR features relative to no features. Fifty-four older adults with mild-to-moderate hearing loss completed a single-blinded crossover trial. Two HA models, one a less-expensive, basic-level device (basic HA) and the other a more-expensive, advanced-level device (premium HA), were used. The DM/NR features of the basic HAs (i.e., basic features) were adaptive DMs and gain-reduction NR with fewer channels. In contrast, the DM/NR features of the premium HAs (i.e., premium features) included adaptive DMs and gain-reduction NR with more channels, bilateral beamformers, speech-seeking DMs, pinna-simulation directivity, reverberation reduction, impulse NR, wind NR, and spatial NR. The trial consisted of four conditions, which were factorial combinations of HA model (premium versus basic) and DM/NR feature status (on versus off). To blind participants regarding the HA technology, no technology details were disclosed and minimal training on how to use the features was provided. In each condition, participants wore bilateral HAs for 5 weeks. Outcomes regarding speech understanding, listening effort, sound quality, localization, and HA satisfaction were measured using laboratory tests, retrospective self-reports (i.e., standardized questionnaires), and in-situ self-reports (i.e., self-reports completed in the real world in real time). A smartphone-based ecological momentary assessment system was used to collect in-situ self-reports. Laboratory efficacy data generally supported the benefit of premium DM/NR features relative to basic DM/NR, premium HAs relative to basic HAs, and DM/NR features relative to no DM/NR in improving speech understanding and localization performance. Laboratory data also indicated that DM/NR features could improve listening effort and sound quality compared with no features for both basic- and premium-level HAs. For real-world effectiveness, in-situ self-reports first indicated that noisy or very noisy situations did not occur very often in participants' daily lives (10.9% of the time). Although both retrospective and in-situ self-reports indicated that participants were more satisfied with HAs equipped with DM/NR features than without, there was no strong evidence to support the benefit of premium DM/NR features and premium HAs over basic DM/NR features and basic HAs, respectively. Although premium DM/NR features and premium HAs outperformed their basic-level counterparts in well-controlled laboratory test conditions, the benefits were not observed in the real world. In contrast, the effect of DM/NR features relative to no features was robust both in the laboratory and in the real world. Therefore, the present study suggests that although both premium and basic DM/NR technologies evaluated in the study have the potential to improve HA outcomes, older adults with mild-to-moderate hearing loss are unlikely to perceive the additional benefits provided by the premium DM/NR features in their daily lives. Limitations concerning the study's generalizability (e.g., participant's lifestyle) are discussed.

Speech perception in noise: Impact of directional microphones in users of combined electric-acoustic stimulation.

ObjectivesCombined electric-acoustic stimulation (EAS) is a well-accepted therapeutic treatment for cochlear implant (CI) users with residual hearing in the low frequencies but severe to profound hearing loss in the high frequencies. The recently introduced SONNETeas audio processor offers different microphone directionality (MD) settings and wind noise reduction (WNR) as front-end processing. The aim of this study was to compare speech perception in quiet and noise between two EAS audio processors DUET 2 and SONNETeas, to assess the impact of MD and WNR on speech perception in EAS users in the absence of wind. Furthermore, subjective rating of hearing performance was registered.MethodSpeech perception and subjective rating with SONNETeas or DUET 2 audio processor were assessed in 10 experienced EAS users. Speech perception was measured in quiet and in a diffuse noise setup (MSNF). The SONNETeas processor was tested with three MD settings omnidirectional/natural/adaptive and with different intensities of WNR. Subjective rating of auditory benefit and sound quality was rated using two questionnaires.ResultsThere was no significant difference between DUET 2 and SONNETeas processor using the omnidirectional microphone in quiet and in noise. There was a significant improvement in SRT with MD settings natural (2.2 dB) and adaptive (3.6 dB). No detrimental effect of the WNR algorithm on speech perception was found in the absence of wind. Sound quality was rated as “moderate” for both audio processors.ConclusionsThe different MD settings of the SONNETeas can provide EAS users with better speech perception compared to an omnidirectional microphone. Concerning speech perception in quiet and quality of life, the performance of the DUET 2 and SONNETeas audio processors was comparable.

DNN-based speech enhancement using soft audible noise masking for wind noise reduction

This paper presents a deep neural network (DNN)-based speech enhancement algorithm based on the soft audible noise masking for the single-channel wind noise reduction. To reduce the low-frequency residual noise, the psychoacoustic model is adopted to calculate the masking threshold from the estimated clean speech spectrum. The gain for noise suppression is obtained based on soft audible noise masking by comparing the estimated wind noise spectrum with the masking threshold. To deal with the abruptly time-varying noisy signals, two separate DNN models are utilized to estimate the spectra of clean speech and wind noise components. Experimental results on the subjective and objective quality tests show that the proposed algorithm achieves the better performance compared with the conventional DNN-based wind noise reduction method.

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

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.

Wind noise reduction at infrasound frequencies using large domes

One ongoing challenge to the use of infrasound for long-range monitoring applications (e.g., remote explosions or natural sources of infrasound such as tornados, volcanos or hurricanes) continues to be the high noise floors experienced at many permanent infrasound stations. International Monitoring System (IMS) infrasound stations typically address this problem using large pipe arrays as wind noise filters. However, these arrays generate significant distortion of higher frequency signals, making them non-ideal signals with significant frequency content above a few Hz. Further, they do not produce sufficient noise reduction during windy (typically daytime) measurements, making their utility generally limited to very quiet wind-noise periods (typically nighttime). Here we report on the development history of a 20-foot dome intended for permanent infrasound installations, and on measurements collected from arrays at the University of Mississippi Biological Field Station and at the Sandia National Laboratory Facility for Acceptance, Calibration, and Testing (FACT) site. The 20-foot dome at the FACT site is collocated with an IMS 18-m rosette filter and a University of Alaska Fairbanks experimental array, and more than a year of data has been collected, allowing for detailed comparisons of the efficacy of these different wind-noise reduction schemes.

Sub-Band Detector for Wind-Induced Noise

This study considers the problem of reliably detecting wind in portable devices such as mobile phones, personal digital assistants (PDA), tablet computers, and alike. A multi-microphone wind detector is presented that utilizes statistical analysis of microphone signals. This statistical analysis is used to quantify the discrepancy in dynamic behavior of the microphone signals. Empirical Distribution Function (EDF) is proposed to capture the dynamic behavior of each microphone signal, and the discrepancy in this behavior is quantified using mean absolute difference between the corresponding EDFs. A decision about wind presence is made separately for each chosen sub-band in order to avoid performing wind noise reduction in a part of audio spectrum which was not affected by wind-induced noise. The proposed wind detector demonstrates reliable detection of wind for each chosen sub-band in an exemplary mobile phone application. Performance evaluation confirms superiority of the proposed sub-band wind detector in terms of correct detection and false alarm compared to a state-of-the art wind detector.

Spatial decorrelation of wind noise with porous microphone windscreens.

This paper explores the wind noise reduction mechanism of porous microphone windscreens by investigating the spatial correlation of wind noise. First, the spatial structure of the wind noise signal is studied by simulating the magnitude squared coherence of the pressure measured with two microphones at various separation distances, and it is found that the coherence of the two signals decreases with the separation distance and the wind noise is spatially correlated only within a certain distance less than the turbulence wavelength. Then, the wind noise reduction of the porous microphone windscreen is investigated, and the porous windscreen is found to be the most effective in attenuating wind noise in a certain frequency range, where the windscreen diameter is approximately 2 to 4 times the turbulence wavelengths (2 < D0/ξ < 4), regardless of the wind speed and windscreen diameter. The spatial coherence between the wind noise outside and inside a porous microphone windscreen is compared with that without the windscreen, and the coherence is found to decrease significantly when the windscreen diameter is approximately 2 to 4 times the turbulence wavelengths, corresponding to the most effective wind noise reduction frequency range of the windscreen. Experimental results with a fan are presented to support the simulations. It is concluded that the wind noise reduction mechanism of porous microphone windscreens is related to the spatial decorrelation effect on the wind noise signals provided by the porous material and structure.

Use of a Deep Recurrent Neural Network to Reduce Wind Noise: Effects on Judged Speech Intelligibility and Sound Quality.

Despite great advances in hearing-aid technology, users still experience problems with noise in windy environments. The potential benefits of using a deep recurrent neural network (RNN) for reducing wind noise were assessed. The RNN was trained using recordings of the output of the two microphones of a behind-the-ear hearing aid in response to male and female speech at various azimuths in the presence of noise produced by wind from various azimuths with a velocity of 3 m/s, using the “clean” speech as a reference. A paired-comparison procedure was used to compare all possible combinations of three conditions for subjective intelligibility and for sound quality or comfort. The conditions were unprocessed noisy speech, noisy speech processed using the RNN, and noisy speech that was high-pass filtered (which also reduced wind noise). Eighteen native English-speaking participants were tested, nine with normal hearing and nine with mild-to-moderate hearing impairment. Frequency-dependent linear amplification was provided for the latter. Processing using the RNN was significantly preferred over no processing by both subject groups for both subjective intelligibility and sound quality, although the magnitude of the preferences was small. High-pass filtering (HPF) was not significantly preferred over no processing. Although RNN was significantly preferred over HPF only for sound quality for the hearing-impaired participants, for the results as a whole, there was a preference for RNN over HPF. Overall, the results suggest that reduction of wind noise using an RNN is possible and might have beneficial effects when used in hearing aids.

On the wind noise reduction mechanism of porous microphone windscreens.

This paper investigates the wind noise reduction mechanism of porous microphone windscreens. The pressure fluctuations inside the porous windscreens with various viscous and inertial coefficients are studied with numerical simulations. The viscous and inertial coefficients represent the viscous forces resulting from the fluid-solid interaction along the surface of the pores and the inertial forces imposed on the fluid flow by the solid structure of the porous medium, respectively. Simulation results indicate that the wind noise reduction first increases and then decreases with both viscous and inertial coefficients after reaching a maximum. Experimental results conducted on five porous microphone windscreens with porosity from 20 to 60 pores per inch (PPI) show that the 40 PPI windscreen has the highest wind noise reduction performance, and this supports the simulation results. The existence of the optimal values for the viscous and inertial coefficients is explained qualitatively and it is shown that the design of the porous microphone windscreens should take into account both the turbulence suppression inside and the wake generation behind the windscreen to achieve optimal performance.

Health-Related Quality of Life Among Young Children With Cochlear Implants and Developmental Disabilities.

The present study examined differences in health-related quality of life (HRQoL) between deaf children with cochlear implants (CI) with and without developmental disabilities (DD) and differences across HRQoL domains within both groups of children. Ninety-two parents of children with CI aged 3-7 years participated in this cross-sectional study. Of these children, 43 had DD (i.e., CI-DD group) and 49 had no DD or chronic illness, demonstrating overall typical development (i.e., CI-TD group). Parents of children in both groups completed the KINDL, a generic HRQoL questionnaire. Parents also provided anecdotal comments to open-ended questions, and parent comments were evaluated on a CI benefits scale to assess parent-perceived benefits of CI for the deaf children with and without disabilities. Children in the CI-DD group had significantly lower HRQoL compared to children in the CI-TD group, including lower scores on the self-esteem, friend, school, and family HRQoL subscales. No significant differences among groups were found on the physical well-being and emotional well-being subscales. For the CI-TD group, age at implantation correlated negatively with self-esteem and school HRQoL subscales. In the CI-DD group, children's current age correlated negatively with family and with the total HRQoL scores. Parent anecdotal comments and scores on the CI-benefits scale indicated strong parent perceptions of benefits of implantation for children in both groups. Based on parents' proxy report, findings suggest that having DD affects multiple domains of HRQoL among young children with CIs above and beyond that of the CI itself. Parents of deaf children with DD may need greater support through the CI process and follow-up than parents of deaf children without DD.

Wind noise reduction using a compact infrasound sensor array and a Kalman filter based on the Matérn Covariance Function

A method for real-time estimation of stationary infrasound signals such as microbaroms in wind noise at low signal-to-noise ratios using a compact infrasound sensor array is presented. A compact array is defined as a sensor array that has an aperture that is much smaller than the shortest infrasound wavelengths of interest and is unsuitable for estimation of direction-of-arrival. In this application, the spacing between sensors results in the measured wind noise being highly correlated, and therefore, simple averaging cannot be used to obtain a good estimate of the infrasound signal. However, by adequately modeling the spatiotemporal wind noise process, array gain can be realized. The method is based on using a Kalman Filter that is designed with the assumption that the measured wind noise can be adequately modeled as a dynamic Gaussian random field with a Matérn covariance function (demonstrated previously at the ASA Meeting in Salt Lake City, Utah, May, 2016). The presentation describes how to design the Kalman Filter in order to estimate the infrasound signal of interest, demonstrates the filter performance using synthetic and measured data from a compact array, and describes how to extend the method to support changing wind conditions and non-stationary infrasound signals.

Comparison and Validation of Acoustic Response Models for Wind Noise Reduction Pipe Arrays

AbstractThe detection capability of the infrasound component of the International Monitoring System (IMS) is tightly linked to the performance of its wind noise reduction systems. The wind noise reduction solution implemented at all IMS infrasound measurement systems consists of a spatial distribution of air inlets connected to the infrasound sensor through a network of pipes. This system, usually referred to as “pipe array,” has proven its efficiency in operational conditions. The objective of this paper is to present the results of the comparison and validation of three distinct acoustic response models for pipe arrays. The characteristics of the models and the results obtained for a defined set of pipe array configurations are described. A field experiment using a newly developed infrasound generator, dedicated to the validation of these models, is then presented. The comparison between the modeled and empirical acoustic responses shows that two of the three models can be confidently used to estimate pipe array acoustic responses. This study paves the way to the deconvolution of IMS infrasound data from pipe array responses and to the optimization of pipe array design to IMS applications.

Cut-off frequency criterion for wind noise contributions for wind noise reduction enclosures

A cut-off frequency criterion for contributions for the wind noise generated from the undisturbed and surface regions for a wind noise reduction device, such as wind fence enclosures and semi-porous fabric domes, is given. The calculations of the measured wind noise inside an enclosure assume that contributions from the undisturbed region and the surface region dominate and are physically restricted to turbulence wavelengths that are larger than the enclosure and the same size or smaller than the enclosure, respectively. The cut-off frequency criterion defines the wavelength where the dominant source of wind noise transitions from interactions in the undisturbed region to the surface region. The transition point is determined by considering when a volume enclosed by a wavelength, Vλ, is equal to 100 times the volume enclosed by the reduction device, VE. Wind noise calculations following this criterion are presented for a set of semi-porous fabric domes that are 4 m wide and 2.1 m tall, and three cylindrical wind fences that are 5 m wide and 2.9 m tall, 5 m wide and 5.8 m tall, and 10 m wide and 2.9 m tall.