A sound level weighting method for comparing subjective annoyance levels in train station halls

Abstract

This invention discloses a sound level weighting method for comparing subjective annoyance in train station halls. The method comprises the following steps: S1: obtaining a data set of traffic track noise to generate experimental signals; S2: using the experimental signals to conduct subjective annoyance assessments on test subjects; S3: mathematically modeling the annoyance assessment data; S4: validating the evaluation indicators; and S5: proposing a new evaluation network. This invention provides a method for quantifying subjective annoyance in train station halls to improve the waiting environment and facilitate noise management in train halls.

Description

Technical Field

[0001] The present invention relates to the technical field of noise management, and in particular to a sound level weighting method for comparing subjective annoyance levels in train station halls. Background Art

[0002] With the continuous expansion of urban scale and the increasing busyness of urban traffic, the impact of traffic noise on people's health has attracted widespread attention.

[0003] Rail transit noise assessment research predicts and evaluates the acoustic environment caused by rail construction projects during and after their completion. Based on the assessment results, various preventive measures are proposed to inform decision-making by relevant departments. Its fundamental goal is to reduce noise pollution caused by road construction projects to levels permitted by current standards, thereby improving residents' living environment. It also provides a scientific basis for optimizing site selection, building material selection, rational layout, and urban planning for construction projects. Noise affects residents in many ways. Exposure to high levels of noise can cause hearing loss and impact people's physiology, psychology, and normal life. Traffic noise is a random noise with large fluctuations in sound level. Accurately evaluating and measuring its annoyance has always been a challenge for acousticians worldwide.

[0004] The A-weighted sound level (A-weighted sound level) used in related technologies significantly underestimates the impact of low-frequency noise components on annoyance. Therefore, when evaluating noise radiation caused by rail vibration, it cannot accurately reflect residents' subjective annoyance. Therefore, the annoyance level of low-frequency noise caused by rail vibration should be specifically studied to establish an evaluation parameter that accurately reflects the annoyance level of low-frequency noise. Summary of the Invention

[0005] In view of the above-mentioned existing problems, the purpose of the present invention is to provide a sound level weighting method for comparing subjective annoyance levels in train station halls and to propose a new method for quantifying subjective annoyance levels to solve the above-mentioned problems.

[0006] The present invention provides the following technical solutions:

[0007] A sound level weighting method for comparing subjective annoyance in a train station hall is characterized by comprising the following steps: S1: obtaining a traffic track noise dataset to produce an experimental signal; S2: using the experimental signal to conduct a subjective annoyance evaluation on experimenters; S3: mathematically modeling the annoyance evaluation data; S4: verifying the evaluation index; and S5: proposing a new evaluation network.

[0008] Step S1 specifically involves filtering the noise dataset at different frequencies using a filter and adjusting the filtered signal to a corresponding loudness level. When the filter frequency band is between 100 Hz and 1000 Hz, the corresponding adjusted loudness level is between 55 phon and 80 phon. Preferably, the experimental signal length is 4 to 6 seconds.

[0009] Step S2 specifically includes: adjusting the angle and distance between the speaker playing the experimental signal and the subject, and recording the annoyance evaluation value of the subject under the experimental signal.

[0010] Step S3 specifically includes: selecting psychoacoustic indicators: loudness, sharpness, roughness, fluctuation intensity and linear sound pressure level for linear regression analysis, drawing fitting curves of noise loudness level and annoyance at each frequency under different frequency conditions, and noise frequency at each loudness level and annoyance, and calculating the corresponding Pearson correlation coefficient.

[0011] Step S4 specifically includes: obtaining an evaluation value of the uncalculated noise sample through the mathematical model obtained in step S3, and comparing the result with the subjective evaluation value.

[0012] Step S4 is calculated by the formula:

[0013]

[0014]

[0015] The unbiasedness and prediction accuracy of the mathematical model, where y k is the actual experimental value of subjective evaluation, i.e. the original annoyance degree; z k is the predicted value of the model; k = 1, 2..., n is the number of noise samples.

[0016] Step S5 specifically comprises: calculating different loudness levels and linear sound levels of noise at different frequencies at the same annoyance level based on the fitting curves of the noise loudness level and annoyance at each frequency and the noise frequency and annoyance at each loudness level, and subtracting the sound pressure levels measured by the test signals at other frequencies from the sound pressure level at 1000 Hz as a reference to obtain a new weighting.

[0017] The beneficial technical effects of the present invention are:

[0018] Compared with the existing A-weighted sound pressure level, the technical solution provided by the present invention can reflect the impact of low-frequency noise on annoyance. In addition, the noise evaluation study of the exhibition hall based on psychoacoustic indicators of the present invention helps to quantitatively evaluate traffic noise and provide a basis for sound environment management. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 11. It is a flow chart of a sound level weighting method for comparing subjective annoyance levels in a train station hall according to an embodiment of the present invention;

[0020] Figure 2 is a fitting curve of the noise loudness level of each frequency and the average value of the annoyance in the sound level weighting method for comparing the subjective annoyance of the train station hall in an embodiment of the present invention;

[0021] Figure 3 This is a fitting curve of the frequency of each loudness noise and the average annoyance level in the sound level weighting method for comparing the subjective annoyance level in the train station hall according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following embodiments of the present invention are described in detail. The following embodiments are implemented based on the technical solutions of the present invention, and detailed implementation methods and specific operating procedures are given. However, the scope of protection of the present invention is not limited to the following embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0023] Reference herein to an "embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it refer to independent or alternative embodiments that are mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments unless there is a conflict.

[0024] Example

[0025] like Figure 1 As shown, in a preferred embodiment of the present invention, a sound level weighting method for comparing subjective annoyance levels in train station halls is provided, comprising the following steps: S1: obtaining a traffic track noise dataset to produce an experimental signal; S2: using the experimental signal to conduct a subjective annoyance evaluation on the experimenters; S3: mathematically modeling the annoyance evaluation data; S4: verifying the evaluation indicators; and S5: proposing a new evaluation network.

[0026] Step S1 specifically involves filtering the noise dataset at different frequencies and adjusting the filtered signals to corresponding loudness levels. The corresponding adjusted loudness levels for the filter frequencies of 100 Hz, 160 Hz, 200 Hz, 250 Hz, 500 Hz, and 1000 Hz are 55 phon, 60 phon, 65 phon, 70 phon, 75 phon, and 80 phon, respectively. The experimental signal length is 5 seconds.

[0027] Step S2 specifically includes adjusting the angle and distance between the speaker playing the experimental signal and the subject, and recording the subject's annoyance evaluation value under the experimental signal. As shown in Table 1:

[0028] Table 1: Subjective noise annoyance rating scale

[0029] Score describe 1 No worries 2 A little annoying, but it doesn't affect my mood 3 Annoying, but acceptable 4 Very annoying, causing strong subjective discomfort 5 Totally unacceptable, the subjects did not want to be exposed to this kind of noise again

[0030] like Figure 2 、 3 As shown, step S3 specifically includes: selecting psychoacoustic indicators: loudness, sharpness, roughness, fluctuation intensity, and linear sound pressure level for linear regression analysis, drawing fitting curves of the noise loudness level and annoyance at each frequency under different frequency conditions, and the noise frequency at each loudness level and annoyance, and calculating the corresponding Pearson correlation coefficient. The calculation process of the fitting curve is shown in Tables 2 and 3:

[0031] Table 2: Fitting equations for noise loudness level and annoyance at each frequency

[0032] Experimental signal Fitting curve Fitting equation Pearson correlation coefficient 100Hz Linear fitting Y=0.1726x-8.3608 0.8863 160Hz Linear fitting Y=0.1790x-8.4190 0.8995 200Hz Linear fitting Y=0.2019x-9.8507 0.8943 250Hz Linear fitting Y=0.1453x-6.9439 0.8802 500Hz Linear fitting Y=0.1682x-7.5978 0.8554 1000Hz Linear fitting Y=0.1885x-9.1915 0.8883

[0033] Table 3: Fitting curves of noise frequency and annoyance at various loudness levels

[0034] Experimental signal Fitting curve Fitting equation Pearson correlation coefficient 100Hz Linear fitting Y=0.0323x-0.7968 0.7173 160Hz Linear fitting Y=0.0457x-1.6412 0.7855 200Hz Linear fitting Y=0.0552x-2.0063 0.8448 250Hz Linear fitting Y=0.0285x-0.6507 0.7858 500Hz Linear fitting Y=0.0380x-1.1269 0.88273 1000Hz Linear fitting Y=0.0438x-2.1238 0.72065

[0035] Table 3

[0036] The mathematical model obtained in step S3 is used to obtain an evaluation value of the uncalculated noise sample, and the result is compared with the subjective evaluation value.

[0037] Step S4 is calculated by the formula:

[0038]

[0039]

[0040] The unbiasedness and prediction accuracy of the mathematical model, where y k is the actual experimental value of subjective evaluation, i.e. the original annoyance degree; z k is the predicted value of the model; k = 1, 2..., n is the number of noise samples.

[0041] Step S5 specifically involves calculating the different loudness levels and linear sound levels of noise at different frequencies at the same annoyance level based on the fitted curves of the noise loudness level and annoyance at each frequency and the frequency and annoyance of each loudness level. Using the 1000Hz sound pressure level as a benchmark, the sound pressure levels of the test signals at other frequencies are subtracted to obtain a new weighting. When Y = 3, i.e., the quantized annoyance level is 3, the new weighting network for the annoyance level is shown in Table 4:

[0042] Table 4: Noise loudness levels at different frequencies under equal annoyance levels

[0043]

[0044] The above embodiment of the present invention provides a method for quantifying the annoyance level of the supervisor in order to improve the waiting environment, which is helpful for noise management in train exhibition halls.

[0045] The above describes in detail the preferred embodiments of the present invention. It should be understood that numerous modifications and variations based on the concepts of the present invention can be made by those skilled in the art without inventive effort. Therefore, any technical solution that can be derived by those skilled in the art based on the concepts of the present invention through logical analysis, reasoning, or limited experimentation based on the existing technology should be within the scope of protection defined by the claims.

Claims

1. A sound level weighting method for comparing subjective annoyance levels in train station halls, characterized in that: The following steps are involved: S1: Obtain the traffic track noise dataset to create experimental signals; S2: Use the experimental signal to evaluate the subjective annoyance of the experimenters; S3: mathematical modeling of the annoyance evaluation data; S4: Verify the evaluation indicators; S5: Propose a new evaluation network; The step S1 specifically comprises: filtering the noise data set at different frequencies through a filter, and adjusting the filtered signal to a corresponding loudness level; when the filter frequency band is 100 Hz-1000 Hz, the corresponding adjusted loudness level is 55 phon-80 phon; Step S5 specifically includes calculating different loudness levels and linear sound levels of noise at different frequencies at the same annoyance level based on the noise loudness level and annoyance level at each frequency and the fitting curves of the noise frequency and annoyance at each loudness level, and subtracting the sound pressure levels measured from the test signals at other frequencies with the sound pressure level at 1000 Hz as a reference to obtain a new weighting.

2. The sound level weighting method for comparing subjective annoyance levels in train halls according to claim 1 is characterized in that: The experimental signal length is 4-6s.

3. The sound level weighting method for comparing subjective annoyance levels in train halls according to claim 1 is characterized in that: The step S2 specifically includes adjusting the angle and distance between the speaker playing the experimental signal and the subject, and recording the annoyance evaluation value of the subject under the experimental signal.

4. The sound level weighting method for comparing subjective annoyance levels in train halls according to claim 1 is characterized in that: Step S3 specifically includes: selecting psychoacoustic indicators: loudness, sharpness, roughness, fluctuation intensity and linear sound pressure level for linear regression analysis, drawing fitting curves of the noise loudness level and annoyance of each frequency under different frequency conditions, and the noise frequency of each loudness level and annoyance, and calculating the corresponding Pearson correlation coefficient.

5. The sound level weighting method for comparing subjective annoyance levels in train halls according to claim 1 is characterized in that: The step S4 specifically includes: obtaining an evaluation value of the uncalculated noise sample through the mathematical model obtained in the step S3, and comparing the result with the subjective evaluation value.

6. The sound level weighting method for comparing subjective annoyance levels in train halls according to claim 5 is characterized in that: The step S4 is performed by formula: The unbiasedness and prediction accuracy of the mathematical model, where y k is the actual experimental value of subjective evaluation, i.e. the original annoyance degree; z k is the predicted value of the model; k = 1, 2..., n is the number of noise samples.

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