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Noise and Legal Dispute: Applications and Limits of the Italian Stardard UNI/TS 11844

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Abstract
In forensic acoustics, a possible area of analysis is represented by unwanted sound that 1 is perceived as a source of intrusion or disturbance within a certain auditory context. This context 2 is defined as the "auditory scene" and refers to the set of sounds present in a specific environment. 3 The presence of unwanted sounds in the auditory scene can cause a wide range of negative effects, 4 including disturbance, discomfort, moral or immoral harm, and other types of negative impact on the 5 health and well-being of individuals exposed to noise. In 2022, the technical specification UNI/TS 6 11844:2022 dedicated to the measurement and analysis of intrusive noise was published. The standard 7 introduces the concept of intrusive noise and defines its calculation methods based on environmental 8 measurements. The purposes of this technical specification is to provide an objective support to 9 methods already in used in acoustic disputes, where the assessment of the annoyance of a noise is 10 often a subjective evaluation of the technician. This work delves into the application to some real 11 cases, identifying the potentiality and the limits of the standardized method.
Keywords: 
Subject: Environmental and Earth Sciences  -   Pollution

1. Introduction

Noise is, to a great extent, a purely subjective personal phenomena. Perhaps the best definition of it is as an unwanted sound [1].
Noise does, however, have two basic characteristics: the first is the physical phenomenon which can be measured and thus used in technical specification and the second is the psycho acoustical characteristic which attempts to judge the effect of noise on human beings [2].
The quality of life is strongly influenced by the acoustic quality of the buildings in which we live, work, and rest. The perceived well-being in these spaces is heavily conditioned by the presence of noises coming from the outside or adjacent units, as well as by the discomfort derived from excessive internal reverberation [3].
Researchers have been questioning for long time the efficacy of current metrics and measurement methods in representing people’s perception [4,5,6].
In industries that use small cooling fans, fan noise simply interferes with the ability of the people working nearby to concentrate on their work [7].
The factors of greatest importance to the system designer are the psychological influences on the person rather than the physical influences of sound on the human ear [8,9,10].
Many industry represents an important cause of occupational noise-induced hearing loss (NIHL), a significant yet underappreciated problem in many developing countries [11,12,13].
Noise tolerance refers to the vulnerability of an individual to noise. People with reduced noise tolerance may not tolerate sounds at intensity levels considered comfortable by most other people [14].
In [15] the task encompasses a methodical examination of scientific literature, while considering the repercussions of noise exposure and the documented harm to various apparatuses.
The sound that is perceived as disturbance can be described using a combination of objective and subjective parameters.
Objective parameters are defined by the physics of sound and propagation and include features such as intensity, frequency, duration, and directionality of the sound. Subjective parameters, on the other hand, are defined by psychoacoustics and encompass factors like age, gender, personality, expectations, and individual listening experiences [16].
However, the relationship between these objective and subjective parameters is complex and depends on many factors, including the listener’s perception, the context in which the sound occurs, and environmental conditions. For instance, the same sound can be perceived differently by different individuals based on their experiences, moods, and expectations.
Therefore, to assess the impact of a sound on the listener’s perception, it’s necessary to consider both objective and subjective parameters and their intricate interaction.
Soundscape refers to the collection of sounds present in a specific environment or geographic area. Sound intrusiveness, on the other hand, pertains to the subjective perception of a sound as annoying, disruptive, or harmful [17].
This perception can be influenced by the composition of the soundscape that is, the type, quantity, and quality of the sounds present. For example, a sound that is considered pleasant and integrated into the soundscape in one area might be perceived as annoying or intrusive in a different context if it doesn’t blend well with the other sounds present [18].
In general, a healthy and harmonious soundscape should involve a proper distribution of sounds to avoid certain sounds being deemed intrusive and disturbing [19].
The themes of acoustics can be framed within the categories defined by the Law, in which cases and behaviors are configured, considering that the phenomenon of noise is well described by the laws of physics that define its propagation in terms of generation, transmission, and reception, in various acoustic reference contexts. Noise disturbances are among the most common cases and often involve disputes arising in the context of neighborhoods.
In Italian civil law, where the majority of disputes regarding intrusive noise arise, the main reference is Article 844 of the law [20], which deals with the acceptable level of neighboring interferences.
Lastly, in the realm of Italian penal law, the primary reference is Article 659 of the Penal law [21], specifically aimed at protecting against disturbance of public peace and tranquility.
In addition to this legal framework, there exists a substantial system of technical regulations aimed at both standardizing measurement and evaluation methods for noise in specific contexts and defining and calculating significant parameters.
The paper is organized as follows: Section 2 presents the methods, focusing on the case study and measurements. In Section 3, the results of the standardization model is presented. Conclusions are reported in Section 4.

2. Materials and Methods

2.1. Signal detection theory and Intrusive noise

The new technical specification UNI/TS 11844 [22] defines the measurement procedure and evaluation parameters for sound levels generated by one or more specific sources in a given context, with the purpose of objectively and quantitatively assessing the disturbance associated with one or more specific noise sources.
When these noise sources are distinguishable within the environmental context in which they are located, they are called intrusive noise [23].
The intrusiveness of a sound emission S in an acoustic context characterized by preexisting noise N can be characterized in terms of the signal-to-noise ratio (SNR). The sound emission S is considered intrusive when it is distinguishable from the noise N. The human auditory system can be simplified as a system of band-pass filters, where the listener perceives the output signal of the filter system with a predominant contribution from the filter with the highest masking signal-to-noise ratio.
Masking is mainly determined by the sound energy contained in a narrow frequency band centered on the S signal (critical band). The critical band width B shall increase in proportion to the central band frequency throughout the audible frequency range. For frequencies f>500 Hz the critical band width B is approximate with that of bands at 1/3 octave, while for frequencies f<500 Hz B it is almost constant and equal to about 100 Hz.
In the presence of intrusive noise it may be useful to refer to the theory of the "Signal Detection Theory, SDT", applicable to sensory stimuli, including auditory stimuli [24,25,26].
The general premise of the SDT is that decisions of whether or not the S signal is present are made in a context of uncertainty, and the goal of the decision maker is to correctly identify, discriminate the S signal from the N masking noise.
The theory of signal detection (Signal Detection Theory, SDT) is a theoretical framework used to analyze and understand decision-making processes in the presence of uncertainty or noise [27]. It originates from the field of psychology but has been widely applied in various disciplines, including neuroscience, economics, and engineering.
The main objective of signal detection theory is to examine how individuals differentiate between informative signals (also called "signals") and background noise (also called "noise").
In the context of the Signal Detection Theory (SDT), a signal refers to a meaningful stimulus or event that an individual is trying to detect, while noise refers to irrelevant or distracting stimuli.
In the presence of intrusive noise, it is possible to refer to the theory of "Signal Detection Theory, SDT," the concepts of which form the basis of the recent technical specification UNI/TS 11844:2022. This specification aims to provide guidance in selecting methods for investigating and assessing intrusive noise [28].
The evaluation methodology involves measuring the environmental noise and background noise, and then estimating the intrusive noise from the specific source. The analysis procedure consists of estimating the noise from the specific source under examination, denoted as Ls, using the following relationship as in Equation (1):
L A e q s = 10 log 10 ( 10 L A e q a 10 − 10 L A e q r 10 )
where:
  • LA eqa is the equivalent level of environmental noise [dB(A)];
  • LA eqr is the equivalent level of background noise [dB(A)];
Equation (1) provides reliable estimates of Ls for algebraic differences as in Equation (2):
Δ L = L A e q a − L A e q r ⪰ 3
In reality, noise disturbance is not only correlated with the overall A-weighted sound level, but also with the intrusiveness of the noise. The intrusiveness of noise, in turn, depends on many factors, including:
  • The frequency distribution of sound energy (spectrum) from the investigated source in relation to the background noise;
  • The presence of distinct tonal components;
  • The impulsive nature of the noise;
  • The duration of the noise;
  • The investigation period (daytime, nighttime, etc.).
To address this gap, the UNI/TS 11844 standard introduces the Detectability level D’L to estimate the intrusiveness of a specific sound source in relation to the background noise. The estimation of the intrusiveness of the sound emission from the specific source can be managed using "Detection theory," a psychophysical theory that analyzes an observer’s response to signal exposure in the presence of noise. This theory examines the observer’s ability to distinguish the signal from the interfering noise.
The D’L is based on comparing the estimated spectrum for the specific source s (intrusive noise) with the measured spectrum for the background noise r.
For each frequency band, the parameter d’ is determined as shown in Equation (3):
d i = η B W i x L s i 10 L r i 10
where:
  • η represents the assumed efficiency of the human observer, which is taken to be 0.4 (a parameter estimated from the literature);
  • L si represents the estimated band level in dB for the i-th band for s (specific source);
  • L ri represents the estimated band level in dB for the i-th band for r (background noise);
For the cumulative value dc that takes into account the contributions of N frequency bands, Equation (4) is applied:
d c = d 1 2 + d 2 2 + d 3 2 + . . . . + d n 2
The corresponding D’L parameter is obtained as shown in Equation (5):
D L = 10 log 10 d c
The value of D’L is directly proportional to the intrusiveness of the noise from the specific source. In other words, increasing values of D’L correspond to progressively higher levels of intrusiveness.
The introduction of this parameter prevents excessive simplification by considering only a single weighted A-weighted global value and overlooking the frequency characteristics of the compared sounds. As a result, there is a numerical scale for the level of intrusiveness that depends on the difference between the level of intrusive noise and the level of background noise, evaluated for each frequency band of the sounds.
Table 1 shows the indications of Intrusiveness Magnitude reported on the Table 3, UNI/TS 11844:2022.
The calculation of D’L, being based on the signal-to-noise ratio between the spectra of the specific source and the background noise, takes into account the possible presence of tonal components as well as impulsive events that, as known, tend to distribute their energy across all frequency bands. Moreover, the comparison of these spectra allows highlighting the bands with the highest d’ values and guiding any interventions aimed at reducing intrusiveness.

2.2. The measurement and selection of samples

The methodology has been applied to some different types of noise sources. In particular it was applied and analyzed to 5 cases:
  • road traffic noise;
  • railway noise;
  • noise from HVAC system;
  • noise from a laboratory and shop point;
  • noise from an industrial site.
To identify characteristic spectra, preliminary assessments will be necessary regarding the selection of a representative time period for the analyzed events.
Specifically, referring to guidelines provided by UNI/TS 11844 and according to Italin laws in environmental noise control and measurements [29,30,31,32].
The five cases indicated above were analyzed following the same study methodology. The preliminary analysis envisages identifying the hours of activity and, with the exception of road noise, the types of machinery used and the switching on and off times.
For the laboratory activity It was also interesting obtaining information on noise induced by customers and workers. Hourly traffic flows were acquired for the road and railway, as well as other information relating to the seasonal use of the infrastructure.
The measurement operations were carried out following the methods proposed from time to time by the reference UNI or ISO standard.
The general conclusions, which will be exposed later, can be considered the same for the 5 cases studied.
Considering that the method is more critical in the case of noise coming from more random sources, such as industrial or handicraft sources, only case 4 is analyzed in more detail below. The observations can then be extended to the other cases as well.

3. Results

Based on the measured values summarized in the previous section, the process proceeded to determine the critical bands and calculate the Detectability Level D’L. A crucial decision with significant implications for the final result is related to the methods used to determine the 1/3-octave aggregated spectra of the ambient and background noise, following Section 7.2 of UNI/TS 10844.
The standard itself proposes different methodological approaches, leaving it to the technician to choose the strategy based on specific needs and analyzed situations.
Specifically, the standard suggests using:
  • Time-averaged spectrum of measurement;
  • Band percentile levels with values to be defined case by case.
As has already been said several times previously, according to the approach chosen by the technician, the information to be used as input for calculating the intrusiveness of the noise is different.
Some technical choices that the technician could make during operations, according to the national (UNI) and international (EN, ISO, IEC) standard in use, are described below, which will lead to a different result.
  • Choice 1: environmental noise measured continuously from opening to closing and background noise measured in the same period in the closing day;
  • Choice 2: environmental noise measured continuously from opening to closing and background noise calculated as L95 in the same period;
  • Choice 3: environmental noise measured when the main noise sources are on and background noise measured in the same period in the closing day;
  • Choice 4: environmental noise measured when the main noise sources are on and background noise calculated as L95 in the same period;
  • Choice 5: environmental noise measured forcing all the sources working at maximum level and background noise measured in the following minutes forcing the sources to all be switched off.
The Table 2 presents the measured overall sound levels and the calculation of the specific source sound level for each choice.
Based on the chosen representation, Table 3, Table 4, Table 5, Table 6 and Table 7 shows the spectra obtained.
From the spectra, the next step involves estimating the intrusiveness. The values of D’L for the three examined cases are reported in Table 8.
The table above shows which different results are obtained depending on the different approach of the specialized technician. All the results originate from choices made in accordance with the legislation in force. The variability of the result, both for the in-depth case of the laboratory and shop, but also for the other 4 cases studied, depends on several factors. In general these factors could be the following:
  • Measurement time representative of the activity analyzed and of the background;
  • Measurement period (Day/Evening/Night);
  • Difficulty of measuring background noise when the noise source cannot be deactivated;
  • Choice of representative noise spectrum (LAEq/percentile/etc.);
  • Choice of a correct measuring point, representative of the situation to be analysed;
  • In long period measurements (example: road traffic noise) problems in distinguishing the source from the context;
  • Need for ancillary equipment for event recognition.

4. Conclusions

In the context of noise disputes, issues and conditions related to the multisensory perception of discomfort are often highlighted. These fall under the categories of noise annoyance and disturbance. These conditions can be subjects of legal disputes or conflicts among stakeholders, such as residents, industries, or government entities, where noise is considered a form of environmental pollution.
To assess the intrusive effect of noise and analyze complex scenarios characterized by multiple overlapping noise sources or different source configurations, the parameter D’L was introduced by the technical specification UNI/TS 11844:2022. This parameter represents a significant contribution in assessing the amount of disturbance caused by noise in specific situations. The D’L parameter takes into account human perception of noise, considering not only the sound level but also temporal characteristics, spectral structure, and other sound properties. This enables a more accurate assessment of the noise’s effect on perception and human comfort.
Intrusive noise can have both auditory and non-auditory effects on humans that extend beyond direct hearing damage. Annoyance and noise disturbance are common issues associated with intrusive noise. To analyze complex scenarios with multiple noise sources or different configurations, the D’L parameter introduced by the technical specification UNI/TS 11844:2022 provides a more comprehensive evaluation method to understand the effect of noise on human perception and associated discomfort.
Depending on the somewhat subjective approach of the competent technician, the obtained result varies from a low intrusiveness assessment to a high intrusiveness assessment. This demonstrates and confirms that initial choices, usually based on subjective evaluations and grounded in techniques and jurisprudence, can significantly influence the final judgment. Therefore, it is desirable that operational guidelines are established based on the existing appendices in the standard and the experience derived from the initial practical applications. These guidelines should support the selection process, aiming for clarity in input data for D’L calculation.

Author Contributions

This work is the result of a collaborative effort.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Kjellberg, A. Subjective, behavioral and psychophysiological effects of noise. Scandinavian journal of work, environment & health, 1990; 29–38. [Google Scholar]
  2. Blauert, J.; Jekosch, U. Sound-quality evaluation–a multi-layered problem. Acta acustica united with acustica 1997, 83, 747–753. [Google Scholar]
  3. Torresin, S.; Albatici, R.; Aletta, F.; Babich, F.; Oberman, T.; Siboni, S.; Kang, J. Indoor soundscape assessment: A principal components model of acoustic perception in residential buildings. Building and Environment 2020, 182, 107152. [Google Scholar] [CrossRef]
  4. Vardaxis, N.G.; Bard, D.; Persson Waye, K. Review of acoustic comfort evaluation in dwellings—part I: Associations of acoustic field data to subjective responses from building surveys. Building Acoustics 2018, 25, 151–170. [Google Scholar] [CrossRef]
  5. Di Loreto, S.; Serpilli, F.; Lori, V. Soundscape Approach in the Seaport of Ancona: A Case Study. Acoustics. MDPI, 2022, Vol. 4, pp. 492–516.
  6. Hong, J.Y.; Chong, K.H. Designing Public Soundscapes through Social Architecture and Soundscape Approaches: Reflective Review of Architectural Design Studio. Sustainability 2023, 15, 12399. [Google Scholar] [CrossRef]
  7. Bijsterveld, K. Mechanical sound: Technology, culture, and public problems of noise in the twentieth century; MIT press, 2008.
  8. Di Loreto, S.; Serpilli, F.; Lori, V.; Squartini, S. Sound quality evaluation of kitchen hoods. Applied Acoustics 2020, 168, 107415. [Google Scholar] [CrossRef]
  9. Murovec, J.; Čurović, L.; Novaković, T.; Prezelj, J. Psychoacoustic approach for cavitation detection in centrifugal pumps. Applied Acoustics 2020, 165, 107323. [Google Scholar] [CrossRef]
  10. Novaković, T.; Ogris, M.; Prezelj, J. Validating impeller geometry optimization for sound quality based on psychoacoustics metrics. Applied acoustics 2020, 157, 107013. [Google Scholar] [CrossRef]
  11. Ammar, S.; Daud, A.; Ismail, A.F.; Razali, A. Screening for Noise-Induced Hearing Loss among Palm Oil Mill Workers in Peninsular Malaysia: A Comparison across Noise Exposure Levels. Standards 2022, 2, 32–42. [Google Scholar] [CrossRef]
  12. Robinson, T.; Whittaker, J.; Acharya, A.; Singh, D.; Smith, M. Prevalence of noise-induced hearing loss among woodworkers in Nepal: a pilot study. International Journal of Occupational and Environmental Health 2015, 21, 14–22. [Google Scholar] [CrossRef] [PubMed]
  13. Zhou, J.; Shi, Z.; Zhou, L.; Hu, Y.; Zhang, M. Occupational noise-induced hearing loss in China: a systematic review and meta-analysis. BMJ open 2020, 10, e039576. [Google Scholar] [CrossRef] [PubMed]
  14. Gu, J.W.; Halpin, C.F.; Nam, E.C.; Levine, R.A.; Melcher, J.R. Tinnitus, diminished sound-level tolerance, and elevated auditory activity in humans with clinically normal hearing sensitivity. Journal of neurophysiology 2010, 104, 3361–3370. [Google Scholar] [CrossRef] [PubMed]
  15. Luzzi, S.; Busa, L.; Pulella, P.; Arcangeli, G.; Traversini, V. Extra-auditory effects from exposure to noise in working environments.
  16. Preis, A. Intrusive sounds. Applied Acoustics 1987, 20, 101–127. [Google Scholar] [CrossRef]
  17. Grinfeder, E.; Lorenzi, C.; Haupert, S.; Sueur, J. What do we mean by “soundscape”? A functional description. Frontiers in Ecology and Evolution 2022, 10, 894232. [Google Scholar] [CrossRef]
  18. Heinrich, M. What about noise? Perception of noise at the workplace–office study 2021.
  19. Yang, T.; Aletta, F.; Kang, J. Sound environments in large public buildings for crowd transit: A systematic review. Applied Sciences 2021, 11, 3728. [Google Scholar] [CrossRef]
  20. Galasso, G. ; others. Immissioni ex art. 844 cc e immissioni inquinanti. In Letture di giurisprudenza. Percorsi ragionati di diritto civile; IT, 2012; Vol. 2012, pp. 120–129.
  21. Losappio, G. Noise Pollution. ECHR Jurisprudence Facing Off Italian Law and Cases. Quaderni del Dipartimento Jonico-Edizioni Digitali Dipartimento Jonico in" Sistemi Giuridici del Mediterraneo: società, ambiente, culture, 2021. [Google Scholar]
  22. per la misurazione e l’analisi del rumore intrusivo, P. per la misurazione e l’analisi del rumore intrusivo, P. UNI/TS 11844:2022.
  23. Fidell, S.; Teffeteller, S. Scaling the annoyance of intrusive sounds. Journal of Sound and Vibration 1981, 78, 291–298. [Google Scholar] [CrossRef]
  24. Green, D.M.; Swets, J.A. ; others. Signal detection theory and psychophysics; Vol. 1, Wiley New York, 1966. [Google Scholar]
  25. Wickens, T.D. Elementary signal detection theory; Oxford university press, 2001.
  26. McNicol, D. A primer of signal detection theory; Psychology Press, 2005.
  27. Fidell, S.; Pearsons, K.S.; Bennett, R. Prediction of aural detectability of noise signals. Human factors 1974, 16, 373–383. [Google Scholar] [CrossRef]
  28. Pastore, R.; Scheirer, C. Signal detection theory: Considerations for general application. Psychological Bulletin 1974, 81, 945. [Google Scholar] [CrossRef]
  29. acustico, L.Q.S. Recommendation Legge 26 Ottobre 1995, n. 447, 1995.
  30. limite delle sorgenti sonore, V. DPCM 14/11/1997.
  31. di rilevamento e misurazione dell’inquinamento acustico, T. D.M. di rilevamento e misurazione dell’inquinamento acustico, T. D.M. 16/3/1998.
  32. in materia di armonizzazione della normativa nazionale in materia di inquinamento acustico, D. in materia di armonizzazione della normativa nazionale in materia di inquinamento acustico, D. DECRETO LEGISLATIVO 17 febbraio 2017, n. 42.
Table 1. Indications of Intrusiveness Magnitude, Table 3, UNI/TS 11844:2022.
Table 1. Indications of Intrusiveness Magnitude, Table 3, UNI/TS 11844:2022.
Detectability level D’L Magnitude of Intrusiveness
DL ′ <13 Negligible
13< DL ′ <18 Very low
18< DL ′ < 23 Low
23< DL ′ <33 Medium
33< DL ′ <43 High
DL ′ >43 Very high
Table 2. Measured sound levels.
Table 2. Measured sound levels.
Choice LA eqa LA eqr LA eqs
1 28,2 21,4 27,2
2 31,1 19,4 30,8
3 34,6 19,1 34,5
4 34,6 31,1 32,0
5 49,9 21,1 49,9
Table 3. Sound spectra used for the calculation of Di ′ . [Choice1].
Table 3. Sound spectra used for the calculation of Di ′ . [Choice1].
Freq L A L ri L si
25 35,8 30,7 34,1
31,5 25,7 22,5 22,9
40 31,9 28,7 29,1
50 35 32,7 32
63 30,3 21,7 29,6
80 38,7 24,5 38,6
100 34,3 26,7 33,5
125 34,1 22,1 33,8
160 34,7 18,4 34,6
200 28,8 16,2 28,6
250 22,9 16,1 21,9
315 24,4 11,2 24,2
400 19,2 12,1 18,2
500 19,4 10,7 18,7
630 17,5 9,5 16,8
800 16,3 7,7 15,7
1000 10,8 6,9 8,6
1250 10,9 6,8 8,7
1600 7,6 7,4 4,6
2000 6,2 7,7 3,2
2500 5,8 7 2,8
3150 6 6,9 3
4000 7,1 7,2 4,1
5000 7,9 8 4,9
6300 10,1 10,1 7,1
8000 9,9 9,9 6,9
10000 10,5 10,6 7,5
Table 4. Sound spectra used for the calculation of Di ′ . [Choice2]
Table 4. Sound spectra used for the calculation of Di ′ . [Choice2]
Freq L A L ri L si
25 35,8 35,4 24,7
31,5 20,9 20,2 12,7
40 25,5 23,9 20,4
50 34,1 31,1 31,4
63 30,3 30,1 15,9
80 41 40,9 20
100 36,4 36 26
125 35,6 35,6 14,6
160 38,5 38,5 12,6
200 32,5 32,4 9,5
250 24,7 24,5 10,8
315 27,8 27,8 7,7
400 21,9 21,8 5,5
500 23,3 23,3 5,9
630 21 20,9 4,6
800 20,5 20,5 3,4
1000 12,9 12,4 3,2
1250 12,8 12,3 3,4
1600 5,7 2,7 3,5
2000 4,6 1,6 3,9
2500 4,7 1,7 4,6
3150 5,5 2,5 5,5
4000 7 4 6,6
5000 7,8 4,8 7,8
6300 10 7 10
8000 9,8 6,8 9,9
10000 10,5 7,5 10,5
Table 5. Sound spectra used for the calculation of Di ′ . [Choice3]
Table 5. Sound spectra used for the calculation of Di ′ . [Choice3]
Freq L A L ri L si
25 39 25 38,8
31,5 23,2 12,4 22,9
40 27,8 19,8 27
50 36,5 29,3 35,6
63 32,1 14,3 32
80 41,1 18,7 41,1
100 35,6 22,2 35,4
125 36,3 14,1 36,3
160 45 11,7 45
200 33,2 8,5 33,2
250 27,4 9,6 27,4
315 35,4 6,4 35,4
400 22 4,5 21,9
500 21,5 4,9 21,4
630 20,8 4 20,7
800 18,2 3,1 18
1000 12,8 2,8 12,4
1250 6,9 3 4,6
1600 4,8 3,3 1,8
2000 4,4 3,8 1,4
2500 4,8 4,6 1,8
3150 5,6 5,5 2,6
4000 7 6,6 4
5000 7,8 7,8 4,8
6300 10 9,9 7
8000 9,9 9,9 6,9
10000 10,5 10,5 7,5
Table 6. Sound spectra used for the calculation of Di ′ . [Choice4]
Table 6. Sound spectra used for the calculation of Di ′ . [Choice4]
Freq L A L ri L si
25 39 34,7 37
31,5 23,2 17,6 21,9
40 27,8 23 26
50 36,5 32 34,7
63 32,1 29,3 29,1
80 41,1 38,3 38,1
100 35,6 33,3 32,6
125 36,3 34,1 33,3
160 45 38,3 44
200 33,2 30,2 30,2
250 27,4 23,8 25
315 35,4 30,4 33,7
400 22 19,7 19
500 21,5 18,6 18,5
630 20,8 17,3 18,2
800 18,2 14,6 15,7
1000 12,8 10,2 9,8
1250 6,9 5 3,9
1600 4,8 3,1 1,8
2000 4,4 3,1 1,4
2500 4,8 4 1,8
3150 5,6 5 2,6
4000 7 6,1 4
5000 7,8 7,1 4,8
6300 10 8,7 7
8000 9,9 9,1 6,9
10000 10,5 10 7,5
Table 7. Sound spectra used for the calculation of Di ′ . [Choice5]
Table 7. Sound spectra used for the calculation of Di ′ . [Choice5]
Freq L A L ri L si
25 1,2 30,7 1,8
31,5 8,4 22,5 5,4
40 13,4 28,7 10,4
50 16,6 32,7 13,6
63 16,6 21,7 13,6
80 17,6 24,5 14,6
100 25,1 26,7 22,1
125 25 22,1 22
160 24 18,4 22,6
200 30 16,2 29,8
250 31 16,1 30,9
315 37 11,2 37
400 30,1 12,1 30,1
500 35 10,7 35
630 37,1 9,5 37,1
800 38,5 7,7 38,5
1000 40,1 6,9 40,1
1250 35,9 6,8 35,9
1600 33,7 7,4 33,7
2000 37,8 7,7 37,8
2500 33,6 7 33,6
3150 34,8 6,9 34,8
4000 33,5 7,2 33,5
5000 35,6 8 35,6
6300 31,9 10,1 31,8
8000 27,1 9,9 27
10000 20,5 10,6 20
Table 8. Obtained D’L values.
Table 8. Obtained D’L values.
Case Detectability level D’L Magnitude of Intrusiveness
1 22 Low
2 31 Medium
3 38 High
4 15 Very Low
5 43 Very High
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