How tonality and loudness of noise relate to annoyance and task performance

Lee, Joon‐Hee; Francis, Jennifer M.; Wang, Lily M.

doi:10.3397/1/376427

Cited by 34 publications

(19 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This result is noteworthy: higher tone strength can affect performance on a digit span task in terms of increasing the total amount of time taken to complete the task, but not its accuracy. This is in line with some other tonal noise studies that investigated performance and did not find significant results in terms of accuracy (Ryherd and Wang, 2010;Lee et al, 2017); none of those studies looked at completion times, though. If subjects can maintain task accuracy under more strongly tonal conditions but at the expense of taking more time to do so, the result is still a significant loss in productivity.…”

Section: A Task Performancesupporting

confidence: 91%

“…Additionally, in the authors' previous studies (Lee et al, 2017), Tonal Audibility was found to be a better indicator than others with regards to correlation to annoyance. Tonal Audibility values in the current study were between 5 and 19 dB.…”

Section: B Equipment and Stimulimentioning

confidence: 68%

“…More and Davies (2010) examined the effects of tones in aircraft noise on human annoyance and found that subjective loudness and tonality both influenced overall annoyance ratings. Previous work by the authors additionally confirmed that both loudness and tonality impact annoyance, although the influence on task performance using Sudoku puzzles was not statistically significant (Francis et al, 2014;Lee et al, 2017); results from testing two tonal frequencies of 125 and 500 Hz were used to develop a model that predicts annoyance of broadband noises with tones. The current paper presents a more extensive investigation that includes a wider number of tonal signals (four tonal frequencies, from 125 Hz to 1 kHz, at five different tonalities above two different ambient noise levels) and links results not only to annoyance responses but also to likelihood to complain.…”

Section: Introductionmentioning

confidence: 87%

See 2 more Smart Citations

Development of a model to predict the likelihood of complaints due to assorted tone-in-noise combinations

Lee

Wang

2018

The Journal of the Acoustical Society of America

Self Cite

View full text Add to dashboard Cite

This paper develops a model to predict if listeners would be likely to complain due to annoyance when exposed to a certain noise signal with a prominent tone, such as those commonly produced by heating, ventilation, and air-conditioning systems. Twenty participants completed digit span tasks while exposed in a controlled lab to noise signals with differing levels of tones, ranging from 125 to 1000 Hz, and overall loudness. After completing the digit span tasks under each noise signal, from which task accuracy and speed of completion were captured, subjects were asked to rate level of annoyance and indicate the likelihood of complaining about the noise. Results show that greater tonality in noise has statistically significant effects on task performance by increasing the time it takes for participants to complete the digit span task; no statistically significant effects were found on task accuracy. A logistic regression model was developed to relate the subjective annoyance responses to two noise metrics, the stationary Loudness and Tonal Audibility, selected for the model due to high correlations with annoyance responses. The percentage of complaints model showed better performance and reliability over the percentage of highly annoyed or annoyed.

show abstract

Section: A Task Performancesupporting

confidence: 91%

Section: B Equipment and Stimulimentioning

confidence: 68%

Section: Introductionmentioning

confidence: 87%

See 1 more Smart Citation

Development of a model to predict the likelihood of complaints due to assorted tone-in-noise combinations

Lee

Wang

2018

The Journal of the Acoustical Society of America

Self Cite

View full text Add to dashboard Cite

show abstract

“…The findings of Lee et al indicated a very high correlation between annoyance responses and loudness metrics while they showed a lower correlation for tonal metrics [20]. Previous studies seriously suggest that when evaluating noise in building mechanical systems, tonality metrics must also be considered, though until now, none of the existing tonal metrics have been extensively used and there is still a limited understanding of the relationship between measurable objective metrics and perceived annoyance.…”

Section: Effects Of Noise On Annoyance and Performancementioning

confidence: 99%

Effects of noise on mental performance and annoyance considering task difficulty level and tone components of noise

Jafari

Sadeghian

Khavanin

et al. 2019

J Environ Health Sci Engineer

View full text Add to dashboard Cite

Rotating components in mechanical systems produce tonal noises and the presence of these tones effect the quality and comfort of occupants leading to annoyance and a decrease in mental performance. The ISO 1996-2 and ANSI S1.13 standards have described metrics to quantify the effects of prominent tones, but more research on how noise attributes effect annoyance and performance, especially in different levels of task difficulty are necessary. This paper investigates relations between noise metrics, annoyance responses and mental performance under different task difficulty levels while exposed to background noise with tonal components. In this study, sixty participants were evaluated on subjective perceived annoyance and varying workloads while exposed to 18 noise signals with three different prominence tones at three frequency tones and two background noise levels while doing three different levels of n-back tasks in a controlled test chamber. Performance parameters were measured by recording the reaction time, the correct rate, and the number of misses. The results indicate an increasing trend for number of misses and reaction times at higher task difficulty levels, but a decrease for correct rate. The study results showed a significant difference for subjective responses except for annoyance and loudness under different levels of task difficulty. The participants were more annoyed with higher background noise levels, lower tone frequencies and increasing tone levels especially under increasing task difficulty. Loudness metrics highly correlate with other noise metrics. Three models for the prediction of perceived annoyance are presented based on the most strongly correlated noise metrics using neural network models. Each of the three models had different input parameters and different network structures. The accuracy and MSE of all three neural network models show it to be appropriate for predicting perceived annoyance. The results show the effect of tonal noise on annoyance and mental performance especially in different levels of task difficulty. The results also suggest that neural network models have high accuracy and efficiency, and can be used to predict noise annoyance. Model 1 is preferred in certain aspects, such as lower input parameters, making it more user-friendly. The best neural network model included both loudness metrics and tonality metrics. It seems that combined metrics have the least importance and are unnecessary in the proposed neural network model.

show abstract

“…Here, loudness seems particularly important. Short-term annoyance was reported to be strongly correlated with perceived or calculated loudness (e.g., [ 46 , 47 , 48 ]). Further, a listening experiment on road traffic noise revealed loudness to be a good predictor for annoyance with stimuli varying in L Aeq and L C-A [ 11 ].…”

Section: Discussionmentioning

confidence: 99%

Effects of Different Spectral Shapes and Amplitude Modulation of Broadband Noise on Annoyance Reactions in a Controlled Listening Experiment

Schäffer

Pieren

Schlittmeier

et al. 2018

IJERPH

View full text Add to dashboard Cite

Environmental noise from transportation or industrial infrastructure typically has a broad frequency range. Different sources may have disparate acoustical characteristics, which may in turn affect noise annoyance. However, knowledge of the relative contribution of the different acoustical characteristics of broadband noise to annoyance is still scarce. In this study, the subjectively perceived short-term (acute) annoyance reactions to different broadband sounds (namely, realistic outdoor wind turbine and artificial, generic sounds) at 40 dBA were investigated in a controlled laboratory listening experiment. Combined with the factorial design of the experiment, the sounds allowed for separation of the effects of three acoustical characteristics on annoyance, namely, spectral shape, depth of periodic amplitude modulation (AM), and occurrence (or absence) of random AM. Fifty-two participants rated their annoyance with the sounds. Annoyance increased with increasing energy content in the low-frequency range as well as with depth of periodic AM, and was higher in situations with random AM than without. Similar annoyance changes would be evoked by sound pressure level changes of up to 8 dB. The results suggest that besides standard sound pressure level metrics, other acoustical characteristics of (broadband) noise should also be considered in environmental impact assessments, e.g., in the context of wind turbine installations.

show abstract

How tonality and loudness of noise relate to annoyance and task performance

Cited by 34 publications

References 2 publications

Development of a model to predict the likelihood of complaints due to assorted tone-in-noise combinations

Development of a model to predict the likelihood of complaints due to assorted tone-in-noise combinations

Effects of noise on mental performance and annoyance considering task difficulty level and tone components of noise

Effects of Different Spectral Shapes and Amplitude Modulation of Broadband Noise on Annoyance Reactions in a Controlled Listening Experiment

Contact Info

Product

Resources

About