Tracking eye and head movements in natural conversational settings: Effects of hearing loss and background noise level

2018 ◽  
Vol 143 (3) ◽  
pp. 1743-1743 ◽  
Author(s):  
Hao Lu ◽  
Martin McKinney ◽  
Tao Zhang ◽  
Andrew J. Oxenham
Keyword(s):  
Hearing Loss ◽  
Noise Level ◽  
Background Noise ◽  
Head Movements ◽  
Background Noise Level

scholarly journals Effects of background noise level on behavioral estimates of basilar-membrane compression

2010 ◽  
Vol 127 (5) ◽  
pp. 3018-3025 ◽  
Author(s):  
Melanie J. Gregan ◽  
Peggy B. Nelson ◽  
Andrew J. Oxenham
Keyword(s):  
Noise Level ◽  
Background Noise ◽  
Basilar Membrane ◽  
Background Noise Level ◽  
Basilar Membrane Compression

Prediction of Threshold Sand Rates from Acoustic Monitors Using Artificial Intelligence

2021 ◽  
Author(s):  
Ronald E. Vieira ◽  
Bohan Xu ◽  
Asad Nadeem ◽  
Ahmed Nadeem ◽  
Siamack A. Shirazi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Noise Level ◽  
Background Noise ◽  
Pipe Wall ◽  
Flow Patterns ◽  
Operating Conditions ◽  
Flow Conditions ◽  
Background Noise Level ◽  
Input Parameters

Abstract Solids production from oil and gas wells can cause excessive damage resulting in safety hazards and expensive repairs. To prevent the problems associated with sand influx, ultrasonic devices can be used to provide a warning when sand is being produced in pipelines. One of the most used methods for sand detection is utilizing commercially available acoustic sand monitors that clamp to the outside of pipe wall and measures the acoustic energy generated by sand grain impacts on the inner side of a pipe wall. Although the transducer used by acoustic monitors is especially sensitive to acoustic emissions due to particle impact, it also reacts to flow induced noise as well (background noise). The acoustic monitor output does not exceed the background noise level until a sufficient sand rate is entrained in the flow that causes a signal output that is higher than the background noise level. This sand rate is referred to as the threshold sand rate or TSR. A significant amount of data has been compiled over the years for TSR at the Tulsa University Sand Management Projects (TUSMP) for various flow conditions with stainless steel pipe material. However, to use this data to develop a model for different flow patterns, fluid properties, pipe, and sand sizes is challenging. The purpose of this work is to develop an artificial intelligence (AI) methodology using machine learning (ML) models to determine TSR for a broad range of operating conditions. More than 250 cases from previous literature as well as ongoing research have been used to train and test the ML models. The data utilized in this work has been generated mostly in a large-scale multiphase flow loop for sand sizes ranging from 25 to 300 μm varying sand concentrations and pipe diameters from 25.4 mm to 101.6 mm ID in vertical and horizontal directions downstream of elbows. The ML algorithms including elastic net, random forest, support vector machine and gradient boosting, are optimized using nested cross-validation and the model performance is evaluated by R-squared score. The machine learning models were used to predict TSR for various velocity combinations under different flow patterns with sand. The sensitivity to changes of input parameters on predicted TSR was also investigated. The method for TSR prediction based on ML algorithms trained on lab data is also validated on actual field conditions available in the literature. The AI method results reveal a good training performance and prediction for a variety of flow conditions and pipe sizes not tested before. This work provides a framework describing a novel methodology with an expanded database to utilize Artificial Intelligence to correlate the TSR with the most common production input parameters.

Modeling Speech Level as a Function of Background Noise Level and Talker-to-Listener Distance for Talkers Wearing Hearing Protection Devices

2017 ◽  
Vol 60 (12) ◽  
pp. 3393-3403 ◽  
Author(s):  
Rachel E. Bouserhal ◽  
Annelies Bockstael ◽  
Ewen MacDonald ◽  
Tiago H. Falk ◽  
Jérémie Voix
Keyword(s):  
Noise Level ◽  
Background Noise ◽  
Speech Sound ◽  
Sound Level ◽  
Hearing Protection ◽  
Background Noise Level ◽  
Vocal Effort ◽  
Protection Devices ◽  
The Difference ◽  
The Relationship

Purpose Studying the variations in speech levels with changing background noise level and talker-to-listener distance for talkers wearing hearing protection devices (HPDs) can aid in understanding communication in background noise. Method Speech was recorded using an intra-aural HPD from 12 different talkers at 5 different distances in 3 different noise conditions and 2 quiet conditions. Results This article proposes models that can predict the difference in speech level as a function of background noise level and talker-to-listener distance for occluded talkers. The proposed model complements the existing model presented by Pelegrín-García, Smits, Brunskog, and Jeong (2011) and expands on it by taking into account the effects of occlusion and background noise level on changes in speech sound level. Conclusions Three models of the relationship between vocal effort, background noise level, and talker-to-listener distance for talkers wearing HPDs are presented. The model with the best prediction intervals is a talker-dependent model that requires the users' unoccluded speech level at 10 m as a reference. A model describing the relationship between speech level, talker-to-listener distance, and background noise level for occluded talkers could eventually be incorporated with radio protocols to transmit verbal communication only to an intended set of listeners within a given spatial range—this range being dependent on the changes in speech level and background noise level.

Sound environment quality in nursing units in Chinese nursing homes: A pilot study

2020 ◽  
Vol 27 (4) ◽  
pp. 283-298
Author(s):  
Hui Xie ◽  
Bingzhi Zhong ◽  
Chang Liu
Keyword(s):  
Nursing Homes ◽  
Nursing Staff ◽  
Noise Level ◽  
Background Noise ◽  
Subjective Evaluation ◽  
Reverberation Time ◽  
Staff Members ◽  
Background Noise Level ◽  
Sound Environment ◽  
Nursing Station

Recent studies have investigated sound environment in nursing homes. However, there has been little research on the sound environment of nursing units. This research sought to address this gap. Subjective evaluations were gathered using questionnaire surveys of 75 elderly residents and 30 nursing staff members in five nursing units of five nursing homes in Chongqing, China. Background noise level and reverberation time were measured in five empty bedrooms, five occupied bedrooms and five occupied nursing station areas, in five nursing units. The subjective evaluation results indicate that the residents stay in the nursing units for most of their waking hours. The residents and nursing staff had strong preferences for natural sounds, with the lowest perceptions of these in the nursing units. The background noise level in all the occupied bedrooms exceeded Chinese standards for waking and sleeping hours. Only 20% of the occupied nursing station areas were below the allowable noise level for recreation and fitness room during sleeping hours. The nursing station area was identified as the main source of noise in the unit during waking hours. The average background noise level of the occupied bedrooms was 3–12 dBA higher than that of the empty bedrooms during sleeping hours. Attention should be given to the implementation of noise specifications for sleeping hours. The reverberation time of the bedrooms was within the range of 0.44–0.68 s, and in the nursing station areas it was 0.63–1.54 s.

Effects of indoor temperature and background noise on floor impact noise perception

2018 ◽  
Vol 28 (4) ◽  
pp. 454-469 ◽  
Author(s):  
Wonyoung Yang ◽  
Myung-Jun Kim ◽  
Hyeun Jun Moon
Keyword(s):  
Air Temperature ◽  
Noise Level ◽  
Background Noise ◽  
Room Temperature ◽  
Thermal Environment ◽  
Subjective Evaluation ◽  
Adaptation Period ◽  
Indoor Climate ◽  
Impact Noise ◽  
Background Noise Level

This study investigates effects of room air temperature and background noise on the perception of floor impact noises in a room. Floor impact noises were recorded in apartment buildings and were presented in an indoor climate chamber with background noise for subjective evaluation. Thirty-two participants were subjected to all combinations of three thermal conditions (20%C, 25%C, 30%C and relative humidity 50%), four background noise types (Babble, Fan, Traffic and Water), three background noise levels (35 dBA, 40 dBA and 45 dBA) and four floor impact noises (Man Jumping, Children Running, Man Running and Chair Scraping). After a 1-h thermal adaptation period for each thermal condition, the participants were asked to evaluate their thermal and acoustic perceptions. Statistically significant effects were found for the room air temperature and background noise level on the perception of the floor impact noises. Noisiness, loudness and complaints of floor impact noise increased with increasing room temperature and background noise level. Annoyance of floor impact noise showed a peak in acceptable thermal environment for general comfort. Room air temperature was a dominant non-auditory factor contributing to floor impact noise annoyance, while the floor impact noise level influenced the floor impact noise loudness and the floor impact noisiness was almost equally affected by the room temperature, background noise level and floor impact noise level. Further investigation is needed to fully understand the combined perception of floor impact noise under various indoor environmental conditions.

The Effect of Speech Presentation Level on Acceptance of Background Noise in Listeners with Normal Hearing

2006 ◽  
Vol 17 (02) ◽  
pp. 141-146 ◽  
Author(s):  
Clifford A. Franklin ◽  
James W. Thelin ◽  
Anna K. Nabelek ◽  
Samuel B. Burchfield
Keyword(s):  
Noise Level ◽  
Background Noise ◽  
Normal Hearing ◽  
Presentation Level ◽  
Background Noise Level ◽  
Acceptable Noise Level ◽  
The Mean ◽  
The Difference ◽  
Speech Presentation

A method has been established to measure the maximum acceptable background noise level (BNL) for a listener, while listening to speech at the most comfortable listening level (MCL). The acceptable noise level (ANL) is the difference between BNL and MCL. In the present study, the ANL procedure was used to measure acceptance of noise, first, in the presence of speech at MCL and, then, for speech presented at much lower and higher levels in listeners with normal hearing. This study used the term ANL to describe the results obtained at MCL and also at other speech presentation levels. The mean ANL at MCL was 15.5 dB, which is comparable to results obtained by previous investigators. ANL increases systematically with speech presentation level. Mean ANLs ranged from 10.6 dB when speech was presented at 20 dB HL to 24.6 dB when speech was presented at 76 dB HL. The results indicated that the acceptance of noise depends significantly on speech presentation level.

Case 26 increasing VLF background noise level

1997 ◽  
Vol 149 (1) ◽  
pp. 147-157
Author(s):  
T. Yoshino
Keyword(s):  
Noise Level ◽  
Background Noise ◽  
Background Noise Level
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