Sugeno Integrals for the Modelling of Noise Annoyance Aggregation

Verkeyn, Andy; Botteldooren, Dick; Baets, Bernard De; Tré, Guy De

doi:10.1007/3-540-44967-1_33

Cited by 9 publications

(7 citation statements)

References 8 publications

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“…24 In addition to ANN, other models have also been explored in the field of soundscape and community noise. [25][26][27] In this study, factors that may affect the subjective evaluation of sound level and acoustic comfort have first been statistically examined based on the data from large scale field surveys, and the results are used to select appropriate input variables for ANN models. Consequently, prediction models for the subjective evaluation of sound level and acoustic comfort have been developed.…”

Section: Introductionmentioning

confidence: 99%

Modeling subjective evaluation of soundscape quality in urban open spaces: An artificial neural network approach

Kang²

2009

The Journal of the Acoustical Society of America

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This research aims to explore the feasibility of using computer-based models to predict the soundscape quality evaluation of potential users in urban open spaces at the design stage. With the data from large scale field surveys in 19 urban open spaces across Europe and China, the importance of various physical, behavioral, social, demographical, and psychological factors for the soundscape evaluation has been statistically analyzed. Artificial neural network (ANN) models have then been explored at three levels. It has been shown that for both subjective sound level and acoustic comfort evaluation, a general model for all the case study sites is less feasible due to the complex physical and social environments in urban open spaces; models based on individual case study sites perform well but the application range is limited; and specific models for certain types of location/function would be reliable and practical. The performance of acoustic comfort models is considerably better than that of sound level models. Based on the ANN models, soundscape quality maps can be produced and this has been demonstrated with an example.

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Section: Introductionmentioning

confidence: 99%

Modeling subjective evaluation of soundscape quality in urban open spaces: An artificial neural network approach

Kang²

2009

The Journal of the Acoustical Society of America

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“…Since the situations of doubt and reversed preference occur quite frequently in practice (Verkeyn et al 2003), we aim at constructing an algorithm capable of dealing with these situations. Moreover, since mostly it is far from clear which example is the 'wrongdoer', we do not want to remove any examples from the collection of learning examples.…”

Section: The Collection Of Learning Examples (S D) Is Called Monotonmentioning

confidence: 99%

A probabilistic framework for the design of instance-based supervised ranking algorithms in an ordinal setting

2008

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In this article, we present a probabilistic framework which serves as the base from which instance-based algorithms for solving the supervised ranking problem may be derived. This framework constitutes a simple and novel approach to the supervised ranking problem, and we give a number of typical examples of how this derivation can be achieved.In this general framework, we pursue a cumulative and stochastic approach, relying heavily upon the concept of stochastic dominance. We show how the median can be used to extract, in a consistent way, a single (classification) label from a returned cumulative probability distribution function. We emphasize that all operations used are mathematically sound, i.e. they only make use of ordinal properties.Mostly, when confronted with the problem of learning a ranking, the training data is not monotone in itself, and some cleansing operation is performed on it to remove these 'inconsistent' examples. Our framework, however, deals with these occurrences of 'reversed preference' in a non-invasive way. On the contrary, it even allows to incorporate information gained from the occurrence of these reversed preferences. This is exactly what happens in the second realization of the main theorem.

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“…2 , 4 , 12 One of the applicable techniques to analyse the measured data is to model the SPL of various noise sources using genetic algorithm (GA). 13 Mahesh et al 14 found that optimization carried out using GA yields the best optimal solution. Recent studies conducted by Liu et al, 15 Majdi et al 16 and Rashidian et al 17 describe the efficiency of GA in enhancing the ANN performance and improving its drawbacks.…”

Section: Introductionmentioning

confidence: 99%

A genetic algorithm approach for optimization of machinery noise calculations

Rao

Tripathy

2019

Noise & Vibration Worldwide

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Noise produced by various noise sources in the mines is considered as a serious environmental problem. Exposure to such noise levels is considered as hazardous to workers working in such conditions. Noise assessment was exercised in a highly mechanized opencast bauxite mine, located in eastern India according to the Director General of Mines Safety Technical Circular No. 18 of 1975 and No.5 of 1990. There are numerous approaches in the literature on machinery noise prediction based on statistical models, soft computing techniques such as fuzzy inference system, artificial neural networks, support vector machines, adaptive neuro fuzzy inference system and other classification methods. The main drawback of statistical models, fuzzy inference system, artificial neural network, support vector machine and adaptive neuro fuzzy inference system is lack of interpretation for human and optimization issues. An attempt has been made to examine the applicability of a genetic algorithm, to take the advantage of genetic structures to find an optimal sound pressure level of the machinery noise taking into consideration the distance, directivity index, sound power level and other attenuation parameters under several noisy operating conditions according to ISO 9613-2:1996, ISO 6395:2008 and other related standards. Genetic algorithm used in this article has several advantages: it can be applied for low, high and dynamical system and uses a simple procedure to determine the order and the parameters with high accuracy. Genetic algorithm model is trained and tested in MATLAB to find the optimum parameters. Experimental results show that genetic algorithm is able to converge and find the optimum values faster along with acceptable computational time. By comparing the predicted values with the measured values, it proves the effectiveness of the proposed model as a useful and efficient method for machinery noise optimization problems.

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Sugeno Integrals for the Modelling of Noise Annoyance Aggregation

Cited by 9 publications

References 8 publications

Modeling subjective evaluation of soundscape quality in urban open spaces: An artificial neural network approach

Modeling subjective evaluation of soundscape quality in urban open spaces: An artificial neural network approach

A probabilistic framework for the design of instance-based supervised ranking algorithms in an ordinal setting

A genetic algorithm approach for optimization of machinery noise calculations

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