Effect of personality traits on sensitivity, annoyance and loudness perception of low- and high-frequency noise

Abbasi, Milad; Tokhi, M. O.; Falahati, Mohsen; Yazdanirad, Saeid; Ghaljahi, Maryam; Etemadinezhad, Siavash; poshti, Roghay’eh Jaffari Talaar

doi:10.1177/1461348420945818

Cited by 30 publications

(14 citation statements)

References 40 publications

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“…Particularly neuroticism and introversion have been shown to be related to higher sensitivity, perceived loudness, and annoyance induced by high-frequency noise (e.g., 17 ). Hence, in order to make sure that there were no differences in this variable between the groups at baseline, a 30-item version of the five factor personality inventory (NEO-FFI-3) 18 was assessed.…”

Section: Methodsmentioning

confidence: 99%

A longitudinal, randomized experimental pilot study to investigate the effects of airborne ultrasound on human mental health, cognition, and brain structure

Ascone

Kling

Wieczorek

et al. 2021

Sci Rep

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Ultrasound-(US) emitting sources are highly present in modern human environments (e.g., movement sensors, electric transformers). US affecting humans or even posing a health hazard remains understudied. Hence, ultrasonic (22.4 kHz) vs. sham devices were installed in participants’ bedrooms, and active for 28 nights. Somatic and psychiatric symptoms, sound-sensitivity, sleep quality, executive function, and structural MRI were assessed pre-post. Somatization (possible nocebo) and phasic alertness increased significantly in sham, accuracy in a flexibility task decreased significantly in the verum condition (indicating hastier responses). Effects were not sustained after p-level adjustment. Exploratory voxel-based morphometry (VBM) revealed regional grey matter (rGMV) but no regional white matter volume changes in verum (relative to placebo). rGMV increased in bilateral cerebellum VIIb/Crus II and anterior cingulate (BA24). There were rGMV decreases in two bilateral frontal clusters: in the middle frontal gyri/opercular part of inferior frontal gyrus (BA46, 44), and the superior frontal gyri (BA4 ,6, 8). No brain-behavior-links were identified. Given the overall pattern of results, it is suggested that ultrasound may particularly induce regional gray matter decline in frontal areas, however with yet unclear behavioral consequences. Given the localization of clusters, candidate behavioral variables for follow-up investigation are complex motor control/coordination, stress regulation, speech processing, and inhibition tasks.Trial registration: The trial was registered at NIH www.clinicaltrials.gov, trial identifier: NCT03459183, trial name: SonicBrain01, full trial protocol available here: https://clinicaltrials.gov/ct2/show/NCT03459183.

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Section: Methodsmentioning

confidence: 99%

A longitudinal, randomized experimental pilot study to investigate the effects of airborne ultrasound on human mental health, cognition, and brain structure

Ascone

Kling

Wieczorek

et al. 2021

Sci Rep

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“…Then, the music material is cut into 30-s segments. CoolEdit ( Abbasi et al, 2020 ) is used to process the music materials of the original musical instrument.…”

Section: Methodsmentioning

confidence: 99%

Construction of Intelligent Recognition and Learning Education Platform of National Music Genre Under Deep Learning

2022

Front. Psychol.

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In order to study the application of the deep learning (DL) method in music genre recognition, this study introduces the music feature extraction method and the deep belief network (DBN) in DL and proposes the parameter extraction feature and the recognition classification method of an ethnic music genre based on the DBN with five kinds of ethnic musical instruments as the experimental objects. A national musical instrument recognition and classification network structure based on the DBN is proposed. On this basis, a music library classification retrieval learning platform has been established and tested. The results show that, when the DBN only contains one hidden layer and the number of neural nodes in the hidden layer is 117, the basic convergence accuracy is approximately 98%. The first hidden layer has the greatest impact on the prediction results. When the input sample feature size is one-third of the number of nodes in the first hidden layer, the network performance is basically convergent. The DBN is the best way for softmax to identify and classify national musical instruments, and the accuracy rate is 99.2%. Therefore, the proposed DL algorithm performs better in identifying music genres.

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“…Abbasi et al showed that noise sensitivity significantly increased noise annoyance and job stress and also decreased job satisfaction [ 15 ]. This is because noise-sensitive people are vulnerable to noise and other environmental stressors [ 16 ]. Therefore, hospital noise can threaten nurses’ health, their cognitive function and emotion, and in turn, influence the quality of patient care and patient safety.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian network model to predict the role of hospital noise, annoyance, and sensitivity in quality of patient care

et al. 2022

Self Cite

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Background Hospital noise can adversely impact nurses’ health, their cognitive function and emotion and in turn, influence the quality of patient care and patient safety. Thus, the aim of this study was to predict the contributing roles of exposure to hospital noise, staff noise-sensitivity and annoyance, on the quality of patient care. Methods This descriptive and cross-sectional study was carried out among nurses in an Iranian hospital. To determine nurses’ noise exposure level, the noise was measured in 1510 locations across the hospital in accordance with ISO 9612 standards using KIMO DB 300/2 sound level meter and analyzer. An online survey was used to collect nurses’ individual data. Study questionnaires included demographics, Weinstein noise sensitivity scale, noise annoyance scale, and quality of patient care scale. Finally, to analyze the data, Bayesian Networks (BNs), as probabilistic and graphical models, were used. Results For the high noise exposure state, high noise sensitivity, and high annoyance, with the probability of 100%, the probability of delivering a desirable quality of patient care decreased by 21, 14, and 23%, respectively. Moreover, at the concurrently high noise exposure and high noise sensitivity with the probability of 100%, the desirable quality of patient care decreased by 26%. The Bayesian most influence value was related to the association of noise exposure and annoyance (0.636). Moreover, annoyance had the highest association with the physical aspect of quality of care (0.400) and sensitivity had the greatest association with the communication aspect (0.283). Conclusion Annoyance induced from environmental noise and personal sensitivity affected the quality of patient care adversely. Moreover, noise and sensitivity had a separate direct adverse effect upon the quality of patient care, and their co-occurrence reduced the potential for delivering quality patient care.

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Effect of personality traits on sensitivity, annoyance and loudness perception of low- and high-frequency noise

Cited by 30 publications

References 40 publications

A longitudinal, randomized experimental pilot study to investigate the effects of airborne ultrasound on human mental health, cognition, and brain structure

A longitudinal, randomized experimental pilot study to investigate the effects of airborne ultrasound on human mental health, cognition, and brain structure

Construction of Intelligent Recognition and Learning Education Platform of National Music Genre Under Deep Learning

A Bayesian network model to predict the role of hospital noise, annoyance, and sensitivity in quality of patient care

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