Improvement of Zwicker’s psychoacoustic annoyance model aiming at tonal noises

Di, Guoqing; Chen, Xingwang; Song, Kai; Zhou, Bing; Pei, Chunming

doi:10.1016/j.apacoust.2015.12.006

Cited by 62 publications

(48 citation statements)

References 17 publications

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“…Psychoacoustic parameters such as fluctuation strength, roughness, sharpness and loudness [13] are a reliable method of understanding the subjective qualities of sound -so much so that they have been found to show a higher correlation with human perception than physiological measurements [9]. These parameters are responsible for a substantial part of the affect a sound has on a listener -combined, they can model the relative degree of noise annoyance [10], and roughness independently has been found to be a primary component in both natural and artificial alarm sounds [2]. Previous work found that data:sound mappings in which the acoustic parameters were based on psychoacoustic parameters were effective in the context of an astronomical image quality assessment task [14].…”

Section: Doimentioning

confidence: 99%

Investigating Perceptual Congruence between Data and Display Dimensions in Sonification

Ferguson

Brewster

2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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The relationships between sounds and their perceived meaning and connotations are complex, making auditory perception an important factor to consider when designing sonification systems. Listeners often have a mental model of how a data variable should sound during sonification and this model is not considered in most data:sound mappings. This can lead to mappings that are difficult to use and can cause confusion. To investigate this issue, we conducted a magnitude estimation experiment to map how roughness, noise and pitch relate to the perceived magnitude of stress, error and danger. These parameters were chosen due to previous findings which suggest perceptual congruency between these auditory sensations and conceptual variables. Results from this experiment show that polarity and scaling preference are dependent on the data:sound mapping. This work provides polarity and scaling values that may be directly utilised by sonification designers to improve auditory displays in areas such as accessible and mobile computing, process-monitoring and biofeedback.

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Section: Doimentioning

confidence: 99%

Investigating Perceptual Congruence between Data and Display Dimensions in Sonification

Ferguson

Brewster

2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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“…Nilsson’s research also showed that L N had the best correlation with annoyance of noise samples in all single acoustical factors [ 28 ]. Actually, the psychoacoustic annoyance (PA) model [ 17 , 18 ] also showed that loudness was the most important acoustical factor influencing the value of PA (see Equations (1)–(4)). Therefore, to calibrate the annoyance ratings of noise samples from different experimental sample sets, L N was selected to establish a calibration method basing on the relationship between MA and L N in this study, of course, basing on the relationship between another acoustical factor (e.g., L A ) and MA, the calibration procedures described in chapter 2 could also be used to improve the comparability of MA obtained from different experimental sample sets.…”

Section: Resultsmentioning

confidence: 99%

“…Psychoacoustic annoyance (PA) can well estimate the relative annoyance ratings of noise samples [ 17 , 18 ], so the relative magnitude of PA between noise samples is well consistent with the relative magnitude of MA obtained from listening experiments (i.e., the consistency of PA and MA is good). For this reason, PA of all noise samples was calculated by Equations (1)–(4) in this study.…”

Section: Case Studymentioning

confidence: 99%

“…Zwicker [ 17 ] put forward the psychoacoustic annoyance (PA) model in 1999. Then Di [ 18 ] improved the PA model further, considering the tonality of noise. Using this model, relative annoyance degrees of different noises could be calculated directly by acoustical parameters through Equations (1)–(4):

where PA is psychoacoustic annoyance; N 5 is the percentile loudness in sone; w S describes the effect of sharpness S (acum), w FR describes the influence of fluctuation strength F (vacil) and roughness R (asper), and w T describes the effect of tonality T (tu).…”

Section: Introductionmentioning

confidence: 99%

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An Optimization Study on Listening Experiments to Improve the Comparability of Annoyance Ratings of Noise Samples from Different Experimental Sample Sets

Shi

2018

IJERPH

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Annoyance ratings obtained from listening experiments are widely used in studies on health effect of environmental noise. In listening experiments, participants usually give the annoyance rating of each noise sample according to its relative annoyance degree among all samples in the experimental sample set if there are no reference sound samples, which leads to poor comparability between experimental results obtained from different experimental sample sets. To solve this problem, this study proposed to add several pink noise samples with certain loudness levels into experimental sample sets as reference sound samples. On this basis, the standard curve between logarithmic mean annoyance and loudness level of pink noise was used to calibrate the experimental results and the calibration procedures were described in detail. Furthermore, as a case study, six different types of noise sample sets were selected to conduct listening experiments using this method to examine the applicability of it. Results showed that the differences in the annoyance ratings of each identical noise sample from different experimental sample sets were markedly decreased after calibration. The determination coefficient (R2) of linear fitting functions between psychoacoustic annoyance (PA) and mean annoyance (MA) of noise samples from different experimental sample sets increased obviously after calibration. The case study indicated that the method above is applicable to calibrating annoyance ratings obtained from different types of noise sample sets. After calibration, the comparability of annoyance ratings of noise samples from different experimental sample sets can be distinctly improved.

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“…Generated from laboratory-collected data, this model attempts to provide a method to directly calculate the relative annoyance values of singlesource sounds from the psychoacoustic Loudness, Roughness, Sharpness, and Fluctuation Strength. This model has also been further expanded upon to include a term for the Tonality of the sound [31]. However, this model was developed based on laboratory studies of generated, simple sounds (i.e., not real recorded sounds) and does not take into account the semantic information associated with the real environmental sounds present in an urban environment.…”

Section: Annoyance Predictionmentioning

confidence: 99%

Multilevel Annoyance Modelling of Short Environmental Sound Recordings

Orga¹,

Mitchell

Freixes³

et al. 2021

Sustainability

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The recent development and deployment of Wireless Acoustic Sensor Networks (WASN) present new ways to address urban acoustic challenges in a smart city context. A focus on improving quality of life forms the core of smart-city design paradigms and cannot be limited to simply measuring objective environmental factors, but should also consider the perceptual, psychological and health impacts on citizens. This study therefore makes use of short (1–2.7 s) recordings sourced from a WASN in Milan which were grouped into various environmental sound source types and given an annoyance rating via an online survey with N=100 participants. A multilevel psychoacoustic model was found to achieve an overall R2=0.64 which incorporates Sharpness as a fixed effect regardless of the sound source type and Roughness, Impulsiveness and Tonality as random effects whose coefficients vary depending on the sound source. These results present a promising step toward implementing an on-sensor annoyance model which incorporates psychoacoustic features and sound source type, and is ultimately not dependent on sound level.

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Improvement of Zwicker’s psychoacoustic annoyance model aiming at tonal noises

Cited by 62 publications

References 17 publications

Investigating Perceptual Congruence between Data and Display Dimensions in Sonification

Investigating Perceptual Congruence between Data and Display Dimensions in Sonification

An Optimization Study on Listening Experiments to Improve the Comparability of Annoyance Ratings of Noise Samples from Different Experimental Sample Sets

Multilevel Annoyance Modelling of Short Environmental Sound Recordings

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