Journal articles: 'Ambient noise'

1

Fink, Daniel. "Ambient Noise." Hearing Journal 72, no. 3 (March 2019): 18. http://dx.doi.org/10.1097/01.hj.0000554357.99563.e9.

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Carey, William M. "Oceanic ambient noise." Journal of the Acoustical Society of America 110, no. 5 (November 2001): 2689. http://dx.doi.org/10.1121/1.4777239.

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Webster, R. J. "Ambient noise statistics." IEEE Transactions on Signal Processing 41, no. 6 (June 1993): 2249–53. http://dx.doi.org/10.1109/78.218152.

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Krebes, Edward S. "Seismic Ambient Noise." Journal of the Acoustical Society of America 146, no. 1 (July 2019): 532–33. http://dx.doi.org/10.1121/1.5118247.

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Dahl, Peter H., James H. Miller, Douglas H. Cato, and Rex K. Andrew. "Underwater Ambient Noise." Acoustics Today 3, no. 1 (2007): 23. http://dx.doi.org/10.1121/1.2961145.

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Hussain, Yawar, Hernan Martinez-Carvajal, Cristobal Condori, Rogério Uagoda, Martín Cárdenas-Soto, André Luís Brasil Cavalcante, Luciano Soares da Cunha, and Salvatore Martino. "Ambient seismic noise." Terrae Didatica 15 (February 14, 2019): e019012. http://dx.doi.org/10.20396/td.v15i1.8652455.

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Rainfall-induced variations in pore water pressures are the most common sources of significant disasters like landslides, dams’ failure, and other structural collapses. This study aimed at communicating preliminary results of seasonal monitoring of the Sobradinho landslide, Brasilia, Brazil, using ambient seismic noise. The noise was recorded before, during and after the rainy season with three L4-3A seismometers (2Hz) installed in a triangular array at the landslide mass. These records were processed using horizontal to vertical spectral ratio (HVSR) technique. As it resulted from the performed analyses, two frequency (ubiquitous (2Hz) and iniquitous (>2Hz)) peaks were observed over HVSR curves. The fluctuations in the secondary peak of HVSR curves are possibly related to the saturated soil properties of landslide mass in response to seasonal meteorological conditions. Experiments with a denser observation system are required to validate such a further behavior. The proposed method can be used for the monitoring of landslides as well as civil engineering structures like dams, bridges,and building.

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Ermert, Laura, Jonas Igel, Korbinian Sager, Eléonore Stutzmann, Tarje Nissen-Meyer, and Andreas Fichtner. "Introducing noisi: a Python tool for ambient noise cross-correlation modeling and noise source inversion." Solid Earth 11, no. 4 (August 28, 2020): 1597–615. http://dx.doi.org/10.5194/se-11-1597-2020.

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Abstract. We introduce the open-source tool noisi for the forward and inverse modeling of ambient seismic cross-correlations with spatially varying source spectra. It utilizes pre-computed databases of Green's functions to represent seismic wave propagation between ambient seismic sources and seismic receivers, which can be obtained from existing repositories or imported from the output of wave propagation solvers. The tool was built with the aim of studying ambient seismic sources while accounting for realistic wave propagation effects. Furthermore, it may be used to guide the interpretation of ambient seismic auto- and cross-correlations, which have become preeminent seismological observables, in light of nonuniform ambient seismic sources. Written in the Python language, it is accessible for both usage and further development and efficient enough to conduct ambient seismic source inversions for realistic scenarios. Here, we introduce the concept and implementation of the tool, compare its model output to cross-correlations computed with SPECFEM3D_globe, and demonstrate its capabilities on selected use cases: a comparison of observed cross-correlations of the Earth's hum to a forward model based on hum sources from oceanographic models and a synthetic noise source inversion using full waveforms and signal energy asymmetry.

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Rantala, Leena M., Suvi Hakala, Sofia Holmqvist, and Eeva Sala. "Classroom Noise and Teachers' Voice Production." Journal of Speech, Language, and Hearing Research 58, no. 5 (October 2015): 1397–406. http://dx.doi.org/10.1044/2015_jslhr-s-14-0248.

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Purpose The aim of this study was to research the associations between noise (ambient and activity noise) and objective metrics of teachers' voices in real working environments (i.e., classrooms). Method Thirty-two female and 8 male teachers from 14 elementary schools were randomly selected for the study. Ambient noise was measured during breaks in unoccupied classrooms and, likewise, the noise caused by pupils' activity during lessons. Voice samples were recorded before and after a working day. Voice variables measured were sound pressure level (voice SPL), fundamental frequency, jitter, shimmer, and the tilt of the sound spectrum slope (alpha ratio). Results The ambient noise correlated most often with the fundamental frequency of men and voice SPL, whereas activity noise correlated with the alpha ratio and perturbation values. Teachers working in louder ambient noise spoke more loudly before work than those working in lower noise levels. Voice variables generally changed less during work among teachers working in loud activity noise than among those working in lower noise levels. Conclusions Ambient and activity noises affect teachers' voice use. Under loud ambient noise teachers seem to speak habitually loudly, and under loud activity noise teachers' ability to react to loading deteriorates.

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Ross, Donald, Megan F. McKenna, Sean M. Wiggins, and John A. Hildbrand. "Ambient noise bathymetric domains." Journal of the Acoustical Society of America 130, no. 4 (October 2011): 2557. http://dx.doi.org/10.1121/1.3655242.

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Dyer, Ira. "Arctic ambient noise mechanisms." Journal of the Acoustical Society of America 82, S1 (November 1987): S8. http://dx.doi.org/10.1121/1.2025043.

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Yamaguchi, Noboru, and Masao Konomi. "Ambient noise suppression circuit." Journal of the Acoustical Society of America 105, no. 4 (1999): 2071. http://dx.doi.org/10.1121/1.426779.

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12

Snieder, Roel, and Kees Wapenaar. "Imaging with ambient noise." Physics Today 63, no. 9 (September 2010): 44–49. http://dx.doi.org/10.1063/1.3490500.

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13

Johannessen, Ola M., Hanne Sagen, Kenneth V. Starke, Ingjald Engelsen, and Susan Payne. "Ice edge ambient noise." Journal of the Acoustical Society of America 85, S1 (May 1989): S106. http://dx.doi.org/10.1121/1.2026625.

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Poulsen, Andrew J., and Henrik Schmidt. "Changing Arctic ambient noise." Journal of the Acoustical Society of America 141, no. 5 (May 2017): 3654. http://dx.doi.org/10.1121/1.4987911.

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15

Hong, Tae-Kyung, Jeongin Lee, Giha Lee, Junhyung Lee, and Seongjun Park. "Correlation between Ambient Seismic Noises and Economic Growth." Seismological Research Letters 91, no. 4 (June 3, 2020): 2343–54. http://dx.doi.org/10.1785/0220190369.

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Abstract Human activity is a major source of high-frequency seismic noise. Long-term ambient seismic noise levels and their influencing factors are investigated. The diurnal seismic noise level in 5–15 Hz display high correlation with human activities including traffic and industrial operations that are related to economic conditions. The temporal noise-level variations are consistent among three components. Analysis with seismic noises in three consecutive months of each year enables us to estimate the noise levels without seasonal effects. The daytime seismic noise-level changes in major cities of 11 countries are assessed using the 3 month records for decades. The annual seismic noise levels present strong correlations with gross domestic product (GDP), particularly with manufacturing and industrial GDP. The seismic noise levels increase quickly with GDP in low-GDP regions but slowly in high-GDP regions. This is because high-GDP regions already have large volumes of existing noise-inducing sources and because added sources contribute weakly. The seismic noise levels increased by 14%–111% for 5–23 yr depending on the economic conditions. The correlation between ambient seismic noise level and economy growth is a global feature. The high-frequency noise level may be a proxy to present the economic condition. Economic growth affects the Earth environment in a wide range of aspects.

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Fuller, Richard A., Philip H. Warren, and Kevin J. Gaston. "Daytime noise predicts nocturnal singing in urban robins." Biology Letters 3, no. 4 (April 24, 2007): 368–70. http://dx.doi.org/10.1098/rsbl.2007.0134.

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Ambient noise interferes with the propagation of acoustic signals through the environment from sender to receiver. Over the past few centuries, urbanization and the development of busy transport networks have led to dramatic increases in the levels of ambient noise with which animal acoustic communications must compete. Here we show that urban European robins Erithacus rubecula , highly territorial birds reliant on vocal communication, reduce acoustic interference by singing during the night in areas that are noisy during the day. The effect of ambient light pollution, to which nocturnal singing in urban birds is frequently attributed, is much weaker than that of daytime noise.

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Li, Qianqian, Yaxin Liu, Lei Xing, Xiao Han, Yuzhao Lin, Jin Zhang, and Hongmao Zhang. "Analysis of Regional Ambient Seismic Noise in the Chukchi Sea Area in the Arctic Based on OBS Data from the Ninth Chinese National Arctic Scientific Survey." Remote Sensing 15, no. 17 (August 26, 2023): 4204. http://dx.doi.org/10.3390/rs15174204.

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Ambient noise plays a crucial role in influencing the observation quality at seismic stations. By studying the distribution patterns of ambient noise, we can gain initial insights into the noise conditions within a specific research area. This paper investigates the properties of ambient noise in different frequency bands under environmental settings in the Chukchi Sea region, utilizing data collected from ocean bottom seismometers (OBSs) deployed during the Ninth Chinese National Arctic Scientific Survey. The probability density function (PDF) method is used to reveal the distinctive features of ambient noise. In addition, by comparing the crowed number values of ambient noise in the Chukchi Sea area with the global new low-noise model (NLNM) and new high-noise model (NHNM), a more comprehensive understanding of the patterns, distribution characteristics, and sources of ambient noise in the Arctic Chukchi Sea area is gained. The study suggests that the overlying sea ice in the Arctic Chukchi Sea area can suppress the microseismic band ambient noise, and the overall level of ambient noise in the Chukchi Sea area lies between the land seismic ambient noise level and the ambient noise level in the middle- and low-latitude sea areas. Meanwhile, an abnormal power spectrum caused by different levels of natural earthquakes is observed. This study fills the gap by using seafloor seismic instruments to investigate ambient noise in the Chukchi Sea area.

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18

Papsin, Emily, Adrienne L. Harrison, Mattia Carraro, and Robert V. Harrison. "Contralateral Ear Occlusion for Improving the Reliability of Otoacoustic Emission Screening Tests." International Journal of Otolaryngology 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/248187.

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Newborn hearing screening is an established healthcare standard in many countries and testing is feasible using otoacoustic emission (OAE) recording. It is well documented that OAEs can be suppressed by acoustic stimulation of the ear contralateral to the test ear. In clinical otoacoustic emission testing carried out in a sound attenuating booth, ambient noise levels are low such that the efferent system is not activated. However in newborn hearing screening, OAEs are often recorded in hospital or clinic environments, where ambient noise levels can be 60–70 dB SPL. Thus, results in the test ear can be influenced by ambient noise stimulating the opposite ear. Surprisingly, in hearing screening protocols there are no recommendations for avoiding contralateral suppression, that is, protecting the opposite ear from noise by blocking the ear canal. In the present study we have compared transient evoked and distortion product OAEs measured with and without contralateral ear plugging, in environmental settings with ambient noise levels <25 dB SPL, 45 dB SPL, and 55 dB SPL. We found out that without contralateral ear occlusion, ambient noise levels above 55 dB SPL can significantly attenuate OAE signals. We strongly suggest contralateral ear occlusion in OAE based hearing screening in noisy environments.

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19

Garnier, Josselin. "Passive Communication with Ambient Noise." SIAM Journal on Applied Mathematics 81, no. 3 (January 2021): 814–33. http://dx.doi.org/10.1137/20m1366848.

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20

Sotirin, B., and R. Myers. "Statistics of Arctic ambient noise." Journal of the Acoustical Society of America 92, no. 4 (October 1992): 2343. http://dx.doi.org/10.1121/1.405311.

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21

Marshall, S. W. "Depth Dependence of Ambient Noise." IEEE Journal of Oceanic Engineering 30, no. 2 (April 2005): 275–81. http://dx.doi.org/10.1109/joe.2005.850876.

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22

Ross, D. "Ship Sources of Ambient Noise." IEEE Journal of Oceanic Engineering 30, no. 2 (April 2005): 257–61. http://dx.doi.org/10.1109/joe.2005.850879.

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23

Frison, Ted W., Henry D. I. Abarbanel, Joan Cembrola, and Bert Neales. "Chaos in ocean ambient ‘‘noise’’." Journal of the Acoustical Society of America 99, no. 3 (March 1996): 1527–39. http://dx.doi.org/10.1121/1.414730.

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24

Castillo Moore, Kristy, and Steven E. Crocker. "Hydrophone calibration using ambient noise." Journal of the Acoustical Society of America 132, no. 3 (September 2012): 2057. http://dx.doi.org/10.1121/1.4755577.

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25

Thorne, Peter D. "Seabed generation of ambient noise." Journal of the Acoustical Society of America 87, no. 1 (January 1990): 149–53. http://dx.doi.org/10.1121/1.399307.

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26

Hollinberger, D. E., and D. W. Bruder. "Ambient noise data logger buoy." IEEE Journal of Oceanic Engineering 15, no. 4 (1990): 286–91. http://dx.doi.org/10.1109/48.103523.

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27

Farrell, Joseph B., and Garry J. Heard. "Ambient noise in Baffin Bay." Journal of the Acoustical Society of America 82, S1 (November 1987): S29. http://dx.doi.org/10.1121/1.2024737.

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28

Miles, Michael W. "Headset for ambient noise suppression." Journal of the Acoustical Society of America 83, no. 6 (June 1988): 2472. http://dx.doi.org/10.1121/1.396302.

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Urick, Robert, and W. A. Kuperman. "Ambient Noise in the Sea." Journal of the Acoustical Society of America 86, no. 4 (October 1989): 1626. http://dx.doi.org/10.1121/1.398683.

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30

Macaulay, Michael C. "Biological high‐frequency ambient noise." Journal of the Acoustical Society of America 104, no. 3 (September 1998): 1826. http://dx.doi.org/10.1121/1.423474.

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31

Gerstoft, Peter, and Martin Siderius. "Remote sensing with ambient noise." Journal of the Acoustical Society of America 126, no. 4 (2009): 2254. http://dx.doi.org/10.1121/1.3249267.

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Hatcher, Matthew, Alex E. Hay, and John E. Hughes Clarke. "Ambient noise from turbidity currents." Journal of the Acoustical Society of America 141, no. 5 (May 2017): 3717. http://dx.doi.org/10.1121/1.4988134.

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Schimmel, M., E. Stutzmann, F. Ardhuin, and J. Gallart. "Polarized Earth's ambient microseismic noise." Geochemistry, Geophysics, Geosystems 12, no. 7 (July 2011): n/a. http://dx.doi.org/10.1029/2011gc003661.

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Harrison, Chris H. "Geoacoustic inversion of ambient noise." Journal of the Acoustical Society of America 112, no. 5 (November 2002): 2282. http://dx.doi.org/10.1121/1.4808591.

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Meinke, Deanna K., and William Hal Martin. "Boothless audiometry: Ambient noise considerations." Journal of the Acoustical Society of America 153, no. 1 (January 2023): 26–39. http://dx.doi.org/10.1121/10.0016362.

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Ambient noise in the test environment will impact signal detection during hearing threshold measurements due to psychoacoustic masking effects. Technical standards specify the maximum permissible ambient noise levels (MPANLs) for use during audiometric testing. MPANLs are dependent on several factors, including transducer characteristics (supra-aural, circumaural, type of ear cushions or earphone enclosures, and insert earphones), the nature of the hearing test being performed (air conduction vs bone conduction and threshold test vs screen at a suprathreshold level), and measurement instrumentation. The nature of the ambient noise (spectrum and constant vs variable) at the test site must be determined and continually accounted for during the boothless hearing test procedure. Ambient noise monitoring procedures are reviewed and examples of ambient noise characteristics in real-world settings, where hearing testing might be performed outside of a sound-treated environment, are provided. Practical considerations are presented, including examples of available tools for ambient noise monitoring, selection of test locations, and transducer attenuation. These are discussed in the context of calculating MPANLs and how best to ensure that ambient noise levels are not negatively impacting the validity of hearing thresholds.

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Leonard, Marty L., and Andrew G. Horn. "Ambient noise increases missed detections in nestling birds." Biology Letters 8, no. 4 (February 22, 2012): 530–32. http://dx.doi.org/10.1098/rsbl.2012.0032.

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Ambient noise can mask acoustic cues, making their detection and discrimination difficult for receivers. This can result in two types of error: missed detections, when receivers fail to respond to the appropriate cues, and false alarms, when they respond to inappropriate cues. Nestling birds are error-prone, sometimes failing to beg when parents arrive with food (committing missed detections) or begging in response to stimuli other than a parent's arrival (committing false alarms). Here, we ask whether the frequency of these errors by nestling tree swallows ( Tachycineta bicolor ) increases in the presence of noise. We found that nestlings exposed to noise had more missed detections than their unexposed counterparts. We also found that false alarms remained low overall and did not differ significantly between noise and quiet treatments. Our results suggest that nestlings living in noisy environments may be less responsive to their parents than nestlings in quieter environments.

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Lin, Tingting, Xiaokang Yao, Sijia Yu, and Yang Zhang. "Electromagnetic Noise Suppression of Magnetic Resonance Sounding Combined with Data Acquisition and Multi-Frame Spectral Subtraction in the Frequency Domain." Electronics 9, no. 8 (August 5, 2020): 1254. http://dx.doi.org/10.3390/electronics9081254.

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As an advanced groundwater detection method, magnetic resonance sounding (MRS) has received more and more attention. However, the biggest challenge is that MRS measurements always suffer with a bad signal-to-noise ratio (SNR). Aiming at the problem of noise interference in MRS measurement, we propose a novel noise-suppression approach based on the combination of data acquisition and multi-frame spectral subtraction (DA-MFSS). The pure ambient noise from the measurement area is first collected by the receiving coil, and then the noisy MRS signal is recorded following the pulse moments transmitting. The procedure of the pure noise and the noisy MRS signal acquisition will be repeated several times. Then, the pure noise and the noisy signal are averaged to preliminarily suppress the noise. Secondly, the averaged pure noise and the noisy signal are divided into multiple frames. The framed signal is transformed into the frequency domain and the spectral subtraction method is applied to further suppress the electromagnetic noise embedded in the noisy MRS signal. Finally, the de-noised signal is recovered by the overlap-add method and inverse Fourier transformation. The approach was examined by numerical simulation and field measurements. After applying the proposed approach, the SNR of the MRS data was improved by 16.89 dB and both the random noise and the harmonic noise were well suppressed.

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Meng, Chun Xia, Gui Juan Li, and Liang Zhang. "Geo-Acoustic Parameters Inversion from Vertical Coherence Characteristic of Ambient Noise." Applied Mechanics and Materials 385-386 (August 2013): 567–70. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.567.

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In shallow sea the wave of ambient noise contains the information of environment acoustic field. In theory, vertical coherence, which is one of the numerous ambient noise field characters, can be used to invert geo-acoustic parameters because of its relative stability. The ambient noise data were collected in two different seasons using two vertical-laid hydrophones in shallow water. Then acoustic parameters of semi-infinite space seabed are inversed from the vertical coherence of measured ambient noise in the frequency range of 100Hz~3 kHz. The predicted vertical coherence curve of ambient noise, which is computed using inversed parameters, agrees with the measured curve by comparison. The experiment results show that geo-acoustic parameters can be inversed effectively by the vertical coherence of ambient noise.

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MURUGAN, S. SAKTHIVEL, and V. NATARAJAN. "IMPLEMENTATION OF THRESHOLD DETECTION TECHNIQUE FOR EXTRACTION OF COMPOSITE SIGNALS AGAINST AMBIENT NOISES IN UNDERWATER COMMUNICATION USING EMPIRICAL MODE DECOMPOSITION." Fluctuation and Noise Letters 11, no. 04 (December 2012): 1250031. http://dx.doi.org/10.1142/s0219477512500319.

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Acoustic signals transmitted in underwater for distance communication are affected by numerous factors, random events, and corrupted with ambient noise, making them nonlinear and nonstationary in nature. Ambient noises are the background acoustic noises in the sea due to natural and manmade sources like wind, rain, seismic, marine species, harbor activities, motor on the boat, ship traffic, etc. In recent years, the application of Empirical Mode Decomposition (EMD) technique to analyze nonlinear and nonstationary signals has gained importance. In this paper an EMD system is proposed with an algorithm by implementing FFT to identify and extract all the acoustic stationary signals available in the underwater channel that are corrupted due to various ambient noises over a range of 100 Hz to 10 kHz in shallow water region. Further a new threshold detection technique is also incorporated in the algorithm for detection and extraction of composite signals that are not extracted properly. The threshold is calculated using the mean and variance of the noisy signal generated by various ambient noises in the ocean. The algorithm is also validated by transmitting three reference acoustic signals. The proposed EMD approach with threshold detector algorithm identifies and extracts all the signals transmitted along with other stationary signals available in the ocean against various ambient noises.

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Alam, Md Saniul, Lucy Corcoran, Eoin A. King, Aonghus McNabola, and Francesco Pilla. "Modelling of intra-urban variability of prevailing ambient noise at different temporal resolution." Noise Mapping 4, no. 1 (March 28, 2017): 20–44. http://dx.doi.org/10.1515/noise-2017-0002.

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AbstractThe impact of temporal aspects of noise data on model development and intra-urban variability on environmental noise levels are often ignored in the development of models used to predict its spatiotemporal variation within a city. Using a Land Use Regression approach, this study develops a framework which uses routine noise monitors to model the prevailing ambient noise, and to develop a noise variability map showing the variation within a city caused by land-use setting. The impact of data resolution on model development and the impact of meteorological variables on the noise level which are often ignored were also assessed. Six models were developed based on monthly, daily and hourly resolutions of both the noise and predictor data. Cross validation highlighted that only the hourly resolution model having 59%explanatory power of the observed data (adjusted R2) and a potential of explaining at least 0.47% variation of any independent dataset (cross validation R2), was a suitable candidate among all the developed models for explaining intraurban variability of noise.In the hourly model, regions with roads of high traffic volumes, with higher concentrations of heavy goods vehicles, and being close to activity centreswere found to have more impact on the prevailing ambient noise. Road lengthswere found to be the most influential predictors and identified as having an impact on the ambient noise monitors.

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TAI, CHENG-CHI, CHIH-HSING CHANG, CHUAN-CHING TAN, TSUNG-WEN HUANG, and CHING-CHAU SU. "ADAPTIVE BEAMFORMER WITH COMBINATION OF SUBBAND FILTERING FOR HEARING-AID SYSTEMS BACKGROUND NOISE REDUCTION." Biomedical Engineering: Applications, Basis and Communications 14, no. 02 (April 25, 2002): 55–66. http://dx.doi.org/10.4015/s1016237202000097.

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In this paper, we present a noise reduction technique for hearing-aid systems. The proposed algorithm adopted adaptive beamformer with combination of subband filtering technique. The structure of conventional hearing aids is relatively simple. They amplify ambient sounds that include speech signal as well as noise. Because noise and human speech signal are amplified at the same time, hearing-aid users can't clearly hear speech signal in noisy environment. The direction of sound can be used to discriminate speech signal from noise by combining adaptive noise canceller and adaptive beamformer. We have developed a system that based on the constrained adaptive noise canceller to preserve speech signal from straight ahead and minimize background noise arriving from other directions. This system also uses subband filtering technique to reduce the requirement for computation and enhance the flexibility of the system. The performance of this system is illustrated using simulated and real-world noises. The results show that the developed system can reserve the right ahead speech signal and substantially reject noises from other directions.

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Zhu, Shaocheng, Limin Liu, and Zhigang Yao. "The application of threshold empirical mode decomposition de-noising algorithm for battlefield ambient noise." International Journal of Modeling, Simulation, and Scientific Computing 09, no. 04 (August 2018): 1850027. http://dx.doi.org/10.1142/s1793962318500277.

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The detection of the low-altitude acoustic target is an important way to compensate for the weakness of radar. Removing the noise mixed in acoustic signal as much as possible to retain the useful information is a challenging task. Inspired by the wavelet threshold, the de-noising method for low-altitude battlefield acoustic signal based on threshold empirical mode decomposition (EMD-T) is proposed in this paper. Firstly, the noisy signal is decomposed by empirical mode decomposition (EMD) to get the intrinsic mode functions (IMFs). Then the IMFs, whose actual energy exceeds its estimated energy, are processed by the EMD threshold. Finally, the processed IMFs are summed to reconstruct the de-noised signal. To evaluate the performance of the proposed method, extensive simulations are performed using helicopter sound corrupted with four types of typical low-altitude ambient noise under different signal-to-noise ratio (SNR) input values. The performance is evaluated in terms of SNR, root mean square error (RMSE) and smoothness index (SI). The simulations results reveal that the proposed method de-noising method has the perspective of the highest SNR, smallest RMSE and SI in de-noising low-altitude ambient noise compared to other methods, including the wavelet transform (WT) and conventional EMD.

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Halliday, William D., Matthew K. Pine, and Stephen J. Insley. "Underwater noise and Arctic marine mammals: review and policy recommendations." Environmental Reviews 28, no. 4 (December 2020): 438–48. http://dx.doi.org/10.1139/er-2019-0033.

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Underwater noise is an important issue globally. Underwater noise can cause auditory masking, behavioural disturbance, hearing damage, and even death for marine animals. While underwater noise levels have been increasing in nonpolar regions, noise levels are thought to be much lower in the Arctic where the presence of sea ice limits anthropogenic activities. However, climate change is causing sea ice to decrease, which is allowing for increased access for noisy anthropogenic activities. Underwater noise may have more severe impacts in the Arctic compared with nonpolar regions due to a combination of lower ambient sound levels and increased sensitivity of Arctic marine animals to underwater noise. Here, we review ambient sound levels in the Arctic, as well as the reactions of Arctic and sub-Arctic marine mammals to underwater noise. We then relate what is known about underwater noise in the Arctic to policies and management solutions for underwater noise and discuss whether Arctic-specific policies are necessary.

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Ambeth Kumar, V. D., S. Malathi, Abhishek Kumar, Prakash M, and Kalyana C. Veluvolu. "Active Volume Control in Smart Phones Based on User Activity and Ambient Noise." Sensors 20, no. 15 (July 24, 2020): 4117. http://dx.doi.org/10.3390/s20154117.

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To communicate efficiently with a prospective user, auditory interfaces are employed in mobile communication devices. Diverse sounds in different volumes are used to alert the user in various devices such as mobile phones, modern laptops and domestic appliances. These alert noises behave erroneously in dynamic noise environments, leading to major annoyances to the user. In noisy environments, as sounds can be played quietly, this leads to the improper masked rendering of the necessary information. To overcome these issues, a multi-model sensing technique is developed as a smartphone application to achieve automatic volume control in a smart phone. Based on the ambient environment, the volume is automatically controlled such that it is maintained at an appropriate level for the user. By identifying the average noise level of the ambient environment from dynamic microphone and together with the activity recognition data obtained from the inertial sensors, the automatic volume control is achieved. Experiments are conducted with five different mobile devices at various noise-level environments and different user activity states. Results demonstrate the effectiveness of the proposed application for active volume control in dynamic environments.

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Lugli, Marco. "Toward a general model for the evolution of the auditory sensitivity under variable ambient noise conditions." Journal of the Acoustical Society of America 154, no. 4 (October 1, 2023): 2236–55. http://dx.doi.org/10.1121/10.0021306.

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Ambient noise constrains the evolution of acoustic signals and hearing. An earlier fitness model showed that the trade-off between sound detection and recognition helps predict the best level of auditory sensitivity for acoustic communication in noise. Here, the early model is improved to investigate the effects of different noise masking conditions and signal-to-noise ratios (SNRs). It is revealed that low sensitivity is expected for acoustic communication over short distances in complex noisy environments provided missed sound recognition is costly. By contrast, high sensitivity is expected for acoustic communication over long distances in quieter habitats or when sounds are received with good SNRs under unfavorable noise conditions. High sensitivity is also expected in noisy environments characterized by one dominant source of noise with a fairly constant spectrum (running-water noise) or when sounds are processed using anti-masking strategies favoring the detection and recognition of sound embedded in noise. These predictions help explain unexpected findings that do not fit with the current view on the effects of environmental selection on signal and sensitivity. Model predictions are compared with those of models of signal detection in noisy conditions and results of empirical studies.

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Johansson, Anders, Karl Bolin, and Jesper Alvarsson. "Annoyance and Partial Masking of Wind Turbine Noise from Ambient Sources." Acta Acustica united with Acustica 105, no. 6 (November 1, 2019): 1035–41. http://dx.doi.org/10.3813/aaa.919382.

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This paper investigates noise annoyance from wind turbines of different sizes and in different acoustic surroundings. A listening test was conducted where wind turbine noises were rated alone and together with background sounds from a deciduous forest, a busy city and road traffic. A magnitude production procedure was implemented which showed high correlation between repeated measurements and the results were analysed using A-weighted sound levels, signal-to-noise ratios and time varying loudness and partial loudness. Ratings for wind turbine sound heard alone showed no coherent statistically significant differences between wind turbine types, neither for A-weighted sound levels nor loudness. The masking test indicate that road traffic noise is a superior masker compared to forest sound. However, these effects where only statistically significant at low sound levels, below the range 35–45 dB(A), where noise guidelines for wind turbine noise usually are stipulated.

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Won, Yong-Yuk, Sang Min Yoon, and Dongsun Seo. "Ambient LED Light Noise Reduction Using Adaptive Differential Equalization in Li-Fi Wireless Link." Sensors 21, no. 4 (February 4, 2021): 1060. http://dx.doi.org/10.3390/s21041060.

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For Li-Fi wireless links based on a white light emitting diode, an adaptive differential equalization (ADE) technique that reduces various noises such as interference noise and shot one generated from ambient light sources is pro-posed. The ADE technique reduces noise by taking advantage of the fact that the derivative between adjacent sampling points of signal with digital waveform is very different from that of noise with the random analog waveform. Furthermore, a weighting function that reflects the Poisson characteristics of shot noise is applied to the ADE technique in order to maximize the reduction efficiency of ambient noise. The signal-to-noise ratio of input non-return-to-zero-on–off keying (NRZ-OOK) signal is improved by 7.5 dB at the first-generation forward error correction (FEC) threshold (the bit error rate (BER) of 8 × 10−5) using the optical wireless experimental link. In addition, it is confirmed that it is possible to maintain the transmission performance corresponding to the BER of 1 × 10−5 by using the proposed ADE technique, even when the intensity of the ambient light source increases by 6 dB.

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Chiheb, Amira, and Hassina Khelladi. "Performance Comparison of LMS and RLS Algorithms for Ambient Noise Attenuation." International Journal of Electrical and Computer Engineering Research 4, no. 1 (March 15, 2024): 14–19. http://dx.doi.org/10.53375/ijecer.2024.383.

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The aim of this study is to implement two different types of adaptive algorithms for the noise cancellation. The study explores the well-known least mean squares (LMS) adaptive algorithm, which is based on stochastic gradient descent approach, and its performances in terms of noise attenuation level and swiftness in active noise control (ANC). Another algorithm is considered in this investigation based upon the use of the least squares estimation (LSE), commonly named, the recursive least squares algorithm (RLS), and will be compared to the LMS. In order to evaluate the potential of each one, a few simulations are achieved. The numerical experiments are performed by using several real recordings of different environment noises tested on the two proposed adaptive algorithms. A comparison is emphasized regarding noise suppression ability and convergence speed, by implementing both adaptive algorithms on the same noise sources. From this numerical study, the RLS algorithm reveals a faster convergence speed and better control performances than the LMS algorithm.

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Ashokan, Muthuraj, Ganesan Latha, and Ayyadurai Thirunavukkarasu. "Underwater Ambient Noise in Kongsfjorden, Spitsbergen, during the Summers of 2015 and 2016." ARCTIC 73, no. 3 (September 28, 2020): 386–92. http://dx.doi.org/10.14430/arctic70499.

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Underwater ambient noise was measured in Kongsfjorden, Svalbard, during the summers of 2015 and 2016 to understand the contribution of iceberg bubbling, iceberg calving, and shipping noise to the acoustic environment of the fjord. Comparison of the ambient noise data for the months of August, September, and October showed that average noise levels were similar, although the average noise level for 2015 was ~9 dB higher than in 2016 because of higher shipping noise. Maximum ambient noise was produced at frequencies less than 10 kHz during both summers. Spectrograms of iceberg calving noise showed that it occurred in the frequency below 500 Hz. Shipping noise was seen in the band below 600 Hz, and iceberg bubbling noise was detected in the band above 400 Hz. Instrument noise was observed in the frequency 400 Hz. It is clear that ice breaking and shipping contribute substantially to ambient noise in Kongsfjorden.

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Yang, Jie, Stephen Riser, and Eric I. Thorsos. "Open ocean ambient noise data in the frequency band of 100 Hz–50 kHz from the Pacific Ocean: A legacy of Jeffrey A. Nystuen." Journal of the Acoustical Society of America 153, no. 3_supplement (March 1, 2023): A134. http://dx.doi.org/10.1121/10.0018411.

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Ocean ambient noise, spanning from a few hertz to tens of kilohertz, is often the limiting factor for sonar performance in target detection, location, and identification. In this frequency band, wind generated surface breaking waves produce bubbles near the surface that are the dominant ambient noise source. In this work, results from a long-term collaboration between NOAA/ NASA and ambient noise study pioneer, Jeffrey A. Nystuen, are presented. Specifically, two-decades of ambient noise data from six deep ocean moorings with companion surface meteorological measurements are used to validate ambient noise models. Excluding data during rainy periods, the ambient noise level is investigated under different wind speed ranges. For wind speeds exceeding 15 m/s, the ambient noise level displays a sharp drop-off and creates a “cross-over” as the spectral level at higher wind speeds and frequencies becomes lower than that at lower wind speeds. Data-model comparisons show a mismatch, as existing models are monotonic in nature, i.e., the modeled spectral level increases with increasing wind speed for all frequencies. This mismatch, currently under investigation, is likely due to attenuation when ambient sound propagates through the deeper and denser bubble layer under high sea conditions. [Work supported by NOAA, NASA, and ONR.]

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Journal articles on the topic 'Ambient noise'

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