scholarly journals Missing values reconstruction in sound level monitoring station by means of intelligent computing

2018 ◽  
Vol 157 ◽  
pp. 02042
Author(s):  
Leszek Radziszewski ◽  
Michał Kekez ◽  
Alžbeta Sapietová
Keyword(s):  
Missing Values ◽  
Regression Tree ◽  
Sound Level ◽  
Training Dataset ◽  
Intelligent Computing ◽  
Sound Levels ◽  
Level Data ◽  
Short Period ◽  
Computational Intelligence Methods ◽  
Level Monitoring

The aim of the paper was to reconstruct the missing data by applying the model which describes variability of sound level in the whole period from 2013 to 2016. To build the model, the computational intelligence methods, like fuzzy systems, or regression trees can be used. The latter approach was applied and we built the model with Cubist regression tree software, using equivalent sound levels recorded in 2013. For the reconstruction of sound level data in short period of time (several days), time series values and day_of_week values together should be used in the training dataset. For the reconstruction of sound level data in long period of time (several months) day_of_week values should be used in the training dataset.

scholarly journals Model-based imputation of sound level data at thoroughfare using computational intelligence

2021 ◽  
Vol 11 (1) ◽  
pp. 519-527
Author(s):  
Michał Kekez
Keyword(s):  
Random Forest ◽  
Computational Intelligence ◽  
Sound Level ◽  
Training Data ◽  
Training Dataset ◽  
Scaling Functions ◽  
Sound Levels ◽  
Level Data ◽  
Impute Data ◽  
Before And After

Abstract The aim of the paper was to present the methodology of imputation of the missing sound level data, for a period of several months, in many noise monitoring stations located at thoroughfares by applying one model which describes variability of sound level within the tested period. To build the model, at first the proper set of input attributes was elaborated, and training dataset was prepared using recorded equivalent sound levels at one of thoroughfares. Sound level values in the training data were calculated separately for the following 24-hour sub-intervals: day (6–18), evening (18–22) and night (22–6). Next, a computational intelligence approach, called Random Forest was applied to build the model with the aid of Weka software. Later, the scaling functions were elaborated, and the obtained Random Forest model was used to impute data at two other locations in the same city, using these scaling functions. The statistical analysis of the sound levels at the abovementioned locations during the whole year, before and after imputation, was carried out.

scholarly journals Towards the Interpretation of Sound Measurements from Smartphones Collected with Mobile Crowdsensing in the Healthcare Domain: An Experiment with Android Devices

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 170
Author(s):  
Robin Kraft ◽  
Manfred Reichert ◽  
Rüdiger Pryss
Keyword(s):  
Mobile Devices ◽  
Sound Level ◽  
Sensor Data ◽  
Environmental Sound ◽  
Mobile Crowdsensing ◽  
Sound Levels ◽  
Level Data ◽  
Combined Use ◽  
Smartphone Sensors ◽  
Ecological Momentary

The ubiquity of mobile devices fosters the combined use of ecological momentary assessments (EMA) and mobile crowdsensing (MCS) in the field of healthcare. This combination not only allows researchers to collect ecologically valid data, but also to use smartphone sensors to capture the context in which these data are collected. The TrackYourTinnitus (TYT) platform uses EMA to track users’ individual subjective tinnitus perception and MCS to capture an objective environmental sound level while the EMA questionnaire is filled in. However, the sound level data cannot be used directly among the different smartphones used by TYT users, since uncalibrated raw values are stored. This work describes an approach towards making these values comparable. In the described setting, the evaluation of sensor measurements from different smartphone users becomes increasingly prevalent. Therefore, the shown approach can be also considered as a more general solution as it not only shows how it helped to interpret TYT sound level data, but may also stimulate other researchers, especially those who need to interpret sensor data in a similar setting. Altogether, the approach will show that measuring sound levels with mobile devices is possible in healthcare scenarios, but there are many challenges to ensuring that the measured values are interpretable.

Can permanent sensorineural hearing loss be caused by sleeping with an ear against a train window?

2002 ◽  
Vol 116 (9) ◽  
pp. 695-698 ◽  
Author(s):  
Alasdair Robertson ◽  
Brian Bingham ◽  
George McIlwraith
Keyword(s):  
Hearing Loss ◽  
Sensorineural Hearing Loss ◽  
Sound Level ◽  
Sensorineural Hearing ◽  
Sound Levels ◽  
Falling Asleep ◽  
Short Period ◽  
Sufficient Intensity ◽  
Level Meter ◽  
Moving Train

A patient presented to the authors with unilateral sensorineural hearing loss after falling asleep with his ear tightly pressed against a window of a moving train. This study set out to determine whether a train could generate sound levels of sufficient intensity to cause such a hearing loss. A sound level meter was used to measure the sound levels produced at the window of a moving train. Further measurements were made with a rubber attachment on the microphone, that simulated the effect of the ear stuck to the window. The sound levels were found to be amplified by the attachment but not to levels that could cause a hearing loss over a short period. In a second experiment eight healthy volunteers all perceived an increase in sound levels when their ears were pressed against a train window.It seems unlikely that sleeping with an ear against a train window can cause hearing loss, but it cannot be ruled out.

Studying acoustical capacity and quality of verbal communication in occupied restaurants

2021 ◽  
Vol 263 (5) ◽  
pp. 1645-1651
Author(s):  
Jared Paine ◽  
Lily M. Wang
Keyword(s):  
Verbal Communication ◽  
Sound Level ◽  
Reverberation Time ◽  
Sound Levels ◽  
Level Data ◽  
Infrared Cameras ◽  
University Of Nebraska Lincoln ◽  
The University ◽  
Statistical Metrics

Sound level data and occupancy data has been logged in five restaurants by the research team at the University of Nebraska - Lincoln. Sound levels and Occupancy at 10 second intervals were documented over time periods of two to four hours during active business hours. Noise levels were logged with dosimeters distributed throughout each restaurant, and occupancy was obtained from images recorded by infrared cameras. Previous analyses of this data have focused on average sound levels and statistical metrics, such as L10 and L90 values. This presentation focuses on each restaurant's Acoustical Capacity and Quality of Verbal Communication, as introduced by Rindel (2012). Acoustical Capacity is a metric describing the maximum number of persons for reasonable communication in a space, calculated from the unoccupied reverberation time and the volume of the space. Quality of Verbal Communication is a metric describing the ease with which persons in the space can communicate at a singular point in time, depending on the reverberation time, the volume of the space, and the number of occupants in the space.

scholarly journals Application of selected computational intelligence methods to sound level modelling based on traffic intensity in thoroughfare

2019 ◽  
Vol 254 ◽  
pp. 02038 ◽  
Author(s):  
Michał Kekez
Keyword(s):  
Random Forest ◽  
Computational Intelligence ◽  
Sound Pressure ◽  
Prediction Method ◽  
Sound Level ◽  
Pressure Level ◽  
Analytical Models ◽  
Traffic Intensity ◽  
Level Data ◽  
Computational Intelligence Methods

The aim of the paper was to build the models of sound pressure level as a function of traffic intensity in thoroughfare. The models were built by using artificial analytical models or regression trees. The former included Nordic Prediction Method. The latter were represented by Random Forest and Cubist. The analysis of accuracy of all obtained models was conducted. The best models can be used in the process of reconstruction of equivalent sound level data.

scholarly journals The Spatial Selective Auditory Attention of Cochlear Implant Users in Different Conversational Sound Levels

2021 ◽  
Vol 10 (14) ◽  
pp. 3078
Author(s):  
Sara Akbarzadeh ◽  
Sungmin Lee ◽  
Chin-Tuan Tan
Keyword(s):  
Cochlear Implant ◽  
Sound Level ◽  
Auditory Attention ◽  
Impaired Hearing ◽  
Speech Stimuli ◽  
Electric Hearing ◽  
Sound Levels ◽  
Acoustic Hearing ◽  
Speech Envelope ◽  
Different Levels

In multi-speaker environments, cochlear implant (CI) users may attend to a target sound source in a different manner from normal hearing (NH) individuals during a conversation. This study attempted to investigate the effect of conversational sound levels on the mechanisms adopted by CI and NH listeners in selective auditory attention and how it affects their daily conversation. Nine CI users (five bilateral, three unilateral, and one bimodal) and eight NH listeners participated in this study. The behavioral speech recognition scores were collected using a matrix sentences test, and neural tracking to speech envelope was recorded using electroencephalography (EEG). Speech stimuli were presented at three different levels (75, 65, and 55 dB SPL) in the presence of two maskers from three spatially separated speakers. Different combinations of assisted/impaired hearing modes were evaluated for CI users, and the outcomes were analyzed in three categories: electric hearing only, acoustic hearing only, and electric + acoustic hearing. Our results showed that increasing the conversational sound level degraded the selective auditory attention in electrical hearing. On the other hand, increasing the sound level improved the selective auditory attention for the acoustic hearing group. In the NH listeners, however, increasing the sound level did not cause a significant change in the auditory attention. Our result implies that the effect of the sound level on selective auditory attention varies depending on the hearing modes, and the loudness control is necessary for the ease of attending to the conversation by CI users.

scholarly journals Impact of sound levels and patient-related factors on sleep of patients in the intensive care unit: a cross-sectional cohort study

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Piotr F. Czempik ◽  
Agnieszka Jarosińska ◽  
Krystyna Machlowska ◽  
Michał P. Pluta
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Sleep Quality ◽  
Sleep Duration ◽  
Smartphone Application ◽  
Sound Level ◽  
Related Factors ◽  
Cross Sectional ◽  
Sound Levels ◽  
The Impact

Abstract Sleep disruption is common in patients in the intensive care unit (ICU). The aim of the study was to measure sound levels during sleep-protected time in the ICU, determine sources of sound, assess the impact of sound levels and patient-related factors on duration and quality of patients' sleep. The study was performed between 2018 and 2019. A commercially available smartphone application was used to measure ambient sound levels. Sleep duration was measured using the Patient's Sleep Behaviour Observational Tool. Sleep quality was assessed using the Richards-Campbell Sleep Questionnaire (RCSQ). The study population comprised 18 (58%) men and 13 (42%) women. There were numerous sources of sound. The median duration of sleep was 5 (IQR 3.5–5.7) hours. The median score on the RCSQ was 49 (IQR 28–71) out of 100 points. Sound levels were negatively correlated with sleep duration. The cut-off peak sound level, above which sleep duration was shorter than mean sleep duration in the cohort, was 57.9 dB. Simple smartphone applications can be useful to estimate sound levels in the ICU. There are numerous sources of sound in the ICU. Individual units should identify and eliminate their own sources of sound. Sources of sound producing peak sound levels above 57.9 dB may lead to shorter sleep and should be eliminated from the ICU environment. The sound levels had no effect on sleep quality.

Incubator Noise

PEDIATRICS ◽  
1975 ◽  
Vol 56 (4) ◽  
pp. 617-617
Author(s):  
Gōsta Blennow ◽  
Nils W. Svenningsen ◽  
Bengt Almquist
Keyword(s):  
Low Frequency ◽  
Sound Level ◽  
Noise Levels ◽  
Frequency Sound ◽  
Mechanical Noise ◽  
Sound Levels ◽  
Infant Incubator ◽  
Improved Model ◽  
Model C

Recently we reported results from studies of incubator noise levels.1 It was found that in certain types of incubators the noise was considerable, and attention was called to the sound level in the construction of new incubators. Recently we had the opportunity to study an improved model of Isolette Infant Incubator Model C-86 where the mechanical noise from the electrically powered motor has been partially eliminated. With this modification it has been possible to lower the low-frequency sound levels to a certain degree in comparison to the levels registered in our study.

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