A method and system for evaluating sound environment quality of an environmental function area based on subjective satisfaction degree

Abstract

This invention belongs to the technical field of acoustic environment quality assessment for environmental functional zones, and relates to a method and system for assessing acoustic environment quality in environmental functional zones based on subjective satisfaction. The method includes: collecting acoustic environment audio samples from the area to be evaluated at different time periods; extracting acoustic feature parameters of the acoustic environment audio; selecting acoustic feature parameters that are highly correlated with subjective satisfaction, substituting them into a pre-established acoustic quality assessment prediction model to obtain a predicted subjective satisfaction score; and evaluating the acoustic environment quality of the area to be evaluated based on the predicted subjective satisfaction score. The method of this invention can accurately reflect residents' satisfaction with noise, providing more targeted guidance for urban planners and environmental administrators, and helping to reasonably and effectively reduce the impact of noise on residents' lives and improve their quality of life.

Description

Technical Field

[0001] This invention belongs to the field of environmental functional zone acoustic environment quality assessment technology, specifically involving an environmental functional zone acoustic environment quality assessment method and system based on subjective satisfaction. Background Technology

[0002] Due to significant changes in many aspects of noise pollution across my country, exhibiting distinct new characteristics, such as the continuous expansion of noise pollution areas, the emergence of new noise sources, and the high frequency, diversity, and prevalence of various noise pollution complaints, as well as discrepancies between environmental noise monitoring results and public subjective perceptions, traditional sound environment quality assessments primarily focus on measuring physical indicators, such as equivalent sound level, diurnal equivalent sound level, and cumulative percentage sound level (L). 10 L 50 L 90 While these indicators objectively reflect the physical characteristics of sound, such as intensity, they fail to fully capture residents' subjective feelings about the acoustic environment in real-life scenarios. As direct experiencers of the acoustic environment, residents' satisfaction is crucial for assessing the acoustic quality of a region. Existing evaluation systems lack effective consideration from this critical perspective, necessitating a new evaluation method to fill this gap.

[0003] Numerous domestic and international studies have shown that noise has a significant negative impact on residents' satisfaction and annoyance. Residents' subjective satisfaction is closely related to noise characteristics, human physiology and psychology, and the natural and social environment. This complexity has resulted in the lack of a widely accepted and feasible technical standard or norm for noise assessment methods based on residents' subjective satisfaction. In particular, the currently implemented objective noise assessment standards and norms often do not align with residents' subjective perceptions, which is the most significant bottleneck in pollution prevention and control management mechanisms.

[0004] Chinese patent application CN118863122A discloses a method for evaluating the acoustic environment of villages based on multi-source traffic noise and noise annoyance. This patent technology mainly focuses on the acoustic environment evaluation method for multi-source traffic noise, only addressing traffic noise and not evaluating the satisfaction of noise-affected individuals. Chinese patent application CN112906192A discloses a comfort evaluation method considering the coupling of subway station vibration duration and environmental factors. Although this invention introduces a crowd vibration annoyance model, the patent technology only addresses comfort evaluation in subway stations and does not evaluate other types of noise. Summary of the Invention

[0005] In view of the limitations of existing sound environment quality evaluation standards, which are difficult to intuitively and accurately reflect residents' actual subjective satisfaction with noise, the purpose of this invention is to overcome the above-mentioned technical defects and propose a sound environment quality evaluation method for environmental functional zones based on subjective satisfaction, so as to better meet actual needs and improve the scientificity and effectiveness of sound environment quality evaluation.

[0006] In view of this, the present invention proposes a method for evaluating the acoustic environment quality of environmental functional zones based on subjective satisfaction, comprising:

[0007] Step 1: Collect acoustic environment audio samples of the area to be evaluated at different time periods;

[0008] Step 2: Extract the audio acoustic feature parameters of the sound environment;

[0009] Step 3: Select acoustic feature parameters that are highly correlated with subjective satisfaction, and substitute them into the pre-established sound quality evaluation prediction model to obtain the predicted value of subjective satisfaction score.

[0010] Step 4: Evaluate the acoustic environment quality of the area to be evaluated based on the predicted subjective satisfaction score.

[0011] Preferably, before step 1, the method further includes setting up sound environment acquisition points in the area to be evaluated based on the topography, functional zoning, and potential sound source distribution factors of the area to be evaluated.

[0012] Preferably, the acquisition time of the acoustic environment audio sample in step 1 is not less than 1 minute.

[0013] Preferably, the acoustic environmental audio characteristics parameters in step 2 include:

[0014] Basic time-domain parameters, frequency-domain parameters, statistical acoustic parameters, and objective psychoacoustic parameters; among them,

[0015] The basic time-domain parameters include: equivalent continuous A-weighted sound level L. Aeq Equivalent continuous sound level L eq and maximum sound level L max ;

[0016] The frequency domain parameters include: the sound level D centered at 250Hz. 250 The sound level D with the noise frequency centered at 315Hz 315 and the sound level D with the noise frequency centered at 500Hz. 500 ;

[0017] The statistical acoustic parameters include: cumulative 10% sound level (L). 10 50% of the total sound level L 50 90% of the total sound level L90 The difference between the cumulative 10% sound level and the cumulative 90% sound level (L) 10 -L 90 The difference between the cumulative 50% sound level and the cumulative 90% sound level L 50 -L 90 ;

[0018] The objective psychoacoustic parameters include: fluctuation (F), sharpness (S), roughness (R), and loudness (Lou).

[0019] Preferably, the sound quality assessment prediction model in step 3 is:

[0020] F(Sat)=ε+β1*X1+β2*X2+β3*X3+β4*X4+...+β i *X i

[0021] Where F(Sat) is the predicted subjective satisfaction score, ε is the error term, and X1 is the equivalent continuous A-weighted sound level L. Aeq X2 is the sound level centered at 500Hz, and X3 is the difference L between the cumulative 10% and 90% sound levels. 10 -L 90 X4 represents the fluctuation degree F, and β1 to β4 are the coefficients corresponding to each acoustic characteristic parameter. i For other relevant acoustic characteristic parameters, β i For X i The corresponding coefficients have subscripts i greater than 4.

[0022] Preferably, the method further includes a process for establishing a sound quality assessment prediction model, including:

[0023] A large number of ambient sound audio samples were collected. Subjective satisfaction evaluation experiments were conducted on these samples to establish the numerical relationship between the acoustic characteristic parameters of each sound source sample and subjective satisfaction. The acoustic characteristic parameters include: equivalent continuous A-weighted sound level L... Aeq The sound level D with the noise frequency centered at 500Hz 500 The difference between the cumulative 10% sound level and the cumulative 90% sound level L 10 -L 90 The sound quality assessment prediction model was determined by curve fitting, where ε = 5.4929, β1 = -0.1451, β2 = 0.0809, β3 = 0.1714, and β4 = 1.7828.

[0024] Preferably, step 4 includes: making a judgment based on the predicted subjective satisfaction score F(Sat):

[0025] When F(Sat) ≤ 1, the sound quality evaluation grade is seriously dissatisfied;

[0026] When 1 < F(Sat) ≤ 2, the sound quality evaluation grade is relatively dissatisfied;

[0027] When 2 < F(Sat) ≤ 3, the sound quality evaluation grade is slightly dissatisfied;

[0028] When 3 < F(Sat) ≤ 4, the sound quality evaluation grade is fair;

[0029] When 4 < F(Sat) ≤ 5, the sound quality evaluation grade is slightly satisfied;

[0030] When 5 < F(Sat) ≤ 6, the sound quality evaluation grade is relatively satisfied;

[0031] When 6 < F(Sat) ≤ 7, the sound quality evaluation grade is very satisfied;

[0032] When F(Sat) > 5, the area to be evaluated is a quiet area, otherwise it is a non-quiet area.

[0033] In another aspect, the present invention provides a sound environmental quality evaluation system for environmental functional areas based on subjective satisfaction, comprising:

[0034] an acquisition module, configured to acquire acoustic environment audio samples in different time periods of the area to be evaluated;

[0035] an extraction module, configured to extract acoustic characteristic parameters of the acoustic environment audio;

[0036] a score prediction module, configured to select acoustic characteristic parameters with high correlation to subjective satisfaction, substitute the parameters into a pre-established sound quality evaluation prediction model, and obtain a predicted value of the subjective satisfaction score;

[0037] an evaluation module, configured to evaluate the acoustic environmental quality of the area to be evaluated according to the predicted value of the subjective satisfaction score.

[0038] Compared with the prior art, the advantages of the present invention are:

[0039] In actual acoustic environment evaluation, the existing acoustic environment evaluation standards cannot accurately reflect residents' subjective satisfaction, and thus cannot effectively guide environmental governance to meet residents' needs. The present invention evaluates the acoustic environmental quality of noise based on residents' subjective satisfaction, which can more accurately reflect residents' satisfaction with noise. This provides more targeted guidance for urban planners and environmental governance workers, helps to reasonably and effectively reduce the impact of noise on residents' lives, and improves residents' quality of life. Description of Drawings

[0040] Figure 1This is a flowchart of the method for evaluating the acoustic environment quality of environmental functional zones based on subjective satisfaction, as proposed in this invention. Detailed Implementation

[0041] The method of the present invention includes:

[0042] (1) Based on the functional zoning, topography and potential sound source distribution in the area to be evaluated, noise sample collection points are set up scientifically and reasonably, and noise audio samples are collected at different time periods.

[0043] (2) Using acoustic analysis tools, analyze the acoustic feature parameters of each audio sample and extract the key feature parameters that can characterize each noise characteristic.

[0044] (3) Select acoustic feature parameters that are highly correlated with subjective satisfaction and substitute them into the sound quality evaluation prediction model to obtain the predicted value of subjective satisfaction score.

[0045] (4) Evaluate the acoustic environment quality of the area to be evaluated based on the noise and acoustic environment satisfaction classification standards.

[0046] In step 2 above, the analyzed audio feature parameters include basic time-domain parameters: equivalent continuous A-weighted sound level (L... Aeq ), equivalent continuous sound level (L) eq ), maximum sound level (L max Frequency domain parameter: D 250 (Sound level centered at 250Hz) D 315 (Sound level centered at 315Hz) D 500 (Sound level centered at 500Hz); Statistical acoustic parameters: L 10 (cumulative 10% sound level), L 50 (50% cumulative sound level), L 90 (90% of the total sound level), L 10 -L 90 L 50 -L 90 Psychoacoustic objective parameters: acoustic characteristic parameters such as F (fluctuation), S (sharpness), R (roughness), and Lou (loudness).

[0047] In step 3 above, a large number of residents were invited to participate in the experiment to conduct subjective quantitative evaluation of the collected audio data. Based on the data from the subjective satisfaction evaluation experiment, the correlation between acoustic characteristic parameters and subjective satisfaction was analyzed, and the prediction model for the sound environment quality assessment of quiet areas was derived as follows:

[0048]

[0049] Where ε is the error term, Xi are several acoustic characteristic parameters having great correlation with subjective satisfaction, β i are coefficients corresponding to each acoustic characteristic parameter. n is not limited to 4 items.

[0050] In the above step 4, the noise quality assessment grade is divided into 7 grades: when F(Sat)≤1, the sound quality assessment grade is seriously dissatisfied; when 1<F(Sat)≤2, the sound quality assessment grade is relatively dissatisfied; when 2<F(Sat)≤3, the sound quality assessment grade is slightly dissatisfied; when 3<F(Sat)≤4, the sound quality assessment grade is fair; when 4<F(Sat)≤5, the sound quality assessment grade is slightly satisfied; when 5<F(Sat)≤6, the sound quality assessment grade is relatively satisfied; when 6<F(Sat)≤7, the sound quality assessment grade is very satisfied. If the sound quality assessment grade is relatively satisfied or very satisfied, the area can be identified as a quiet area, otherwise it is a non-quiet area.

[0051] The technical solution of the present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0052] Example 1

[0053] As shown in Figure 1 , Embodiment 1 of the present invention provides a sound environment quality assessment method for environmental functional areas based on subjective satisfaction, and the specific implementation steps are as follows:

[0054] (1) Acoustic environment audio data collection: select high-precision acoustic monitoring equipment that meets composite measurement standards, and calibrate it in advance to ensure the accuracy of test data; according to factors such as the topography, functional division and potential sound source distribution of the area to be assessed, set acoustic environment audio sampling points scientifically and reasonably; since noise changes with time, audio collection shall be carried out in different time periods, such as day and night, peak hours and other time periods; collection is performed according to a predetermined sampling time length at each collection point, and generally the sample duration of each sampling point is not less than 1 minute.

[0055] (2) Extracting acoustic characteristic parameters: analyzing each audio acoustic characteristic parameter through an acoustic analysis tool, and extracting key parameters that can characterize each noise characteristic. The extracted key parameters include basic time-domain parameters: equivalent continuous A-weighted sound level (L Aeq ), equivalent continuous sound level (L eq ), maximum sound level (L max ); frequency-domain parameters: D 250 , D 315 , D 500 ; statistical acoustic parameters: L 10 , L 50 , L 90 , L 10 -L90 L 50 -L 90 Psychoacoustic objective parameters: acoustic characteristic parameters such as F (fluctuation), S (sharpness), R (roughness), and Lou (loudness).

[0056] (3) Calculate the acoustic environment satisfaction of the area to be evaluated: The satisfaction of the area to be evaluated is calculated by using the established acoustic quality evaluation model between the relevant acoustic quality evaluation parameters of the quiet area and the residents' subjective satisfaction. The acoustic quality evaluation model adopts a multidimensional linear function fitting method, which usually refers to multiple linear regression, a linear model used in statistics to predict the relationship between a dependent variable (response variable) and multiple independent variables (explanatory variables). The key acoustic feature parameters extracted in step (2) are substituted into the acoustic quality evaluation prediction model of the quiet area.

[0057] For example, in Implementation Case 1, a large number of ambient sound audio samples were first collected. A subjective satisfaction evaluation experiment was conducted on these samples to establish the numerical relationship between the acoustic characteristic parameters of each sound source sample and the subjective satisfaction level. Sixteen characteristic parameters were selected.

[0058] F i ={f i1 ,...,f ij ,...f i16}={F,Lou,R,S,Ton,L max D 250 D 315 D 500 ,L 10 ,L 50 ,L 90 ,L 10 -L 90 ,L 50 -L 90 ,L Aeq ,L eq}

[0059] A subjective satisfaction evaluation experiment was conducted, obtaining 51 sets of subjective evaluation data. Based on the Pearson correlation coefficient r, the characteristic parameters were evaluated. Correlation coefficient r with satisfaction SAT j (SAT), the calculation formula is as follows:

[0060]

[0061] Where n is the number of acoustic feature parameters, and m is the number of samples in the subjective evaluation satisfaction test.

[0062] It can thus be obtained that the objective acoustic characteristic parameters that have a very significant correlation with the quiet area satisfaction Sat are L Aeq (equivalent A-weighted sound level), fluctuation F, D500 (sound level with noise frequency centered at 500 Hz), L 10 -L 90 , and these four characteristic parameters are selected as Sat characterization parameters to predict the subjective satisfaction Sat value. Curve fitting is performed on Sat, and the F(Sat) model is shown in the following formula:

[0063] F(Sat)=ε+β1*X1+β2*X2+β3*X3+β4*X4

[0064] Wherein: ε=5.4929

[0065] β1=-0.1451

[0066] β2=0.0809

[0067] β3=0.1714

[0068] β4=1.7828

[0069] In one embodiment, the four parameters are included, X1 is L Aeq (equivalent A-weighted sound level), X2 is D 500 (sound level with noise frequency centered at 500 Hz), X3 is L 10 -L 90 (difference between the 10% cumulative sound level and the 90% cumulative sound level), and X4 is the fluctuation F.

[0070] (4) Evaluating the acoustic environmental quality of the area to be evaluated: evaluating the acoustic environment according to the subjective satisfaction value obtained in step 3, the noise quality evaluation grades are divided into 7 levels, when F(Sat)≤1, the acoustic quality evaluation grade is severely dissatisfied; when 1<F(Sat)≤2, the acoustic quality evaluation grade is relatively dissatisfied; when 2<F(Sat)≤3, the acoustic quality evaluation grade is slightly dissatisfied; when 3<F(Sat)≤4, the acoustic quality evaluation grade is general; when ), the acoustic quality evaluation grade is slightly satisfied; when 5<F(Sat)≤6, the acoustic quality evaluation grade is relatively satisfied; when 6<F(Sat)≤7, the acoustic quality evaluation grade is very satisfied. When F(Sat)>5, the area to be evaluated is a quiet area, otherwise it is a non-quiet area.

[0071] In this implementation case, a specific area was selected as the target for on-site noise environmental quality assessment. Five monitoring points were set up at different locations within the assessment area, representing different functional zones. Noise samples were collected for at least one minute each during the day and night, and acoustic characteristic parameters were extracted from the samples. Simultaneously, a subjective noise satisfaction assessment experiment was conducted on residents in this quiet area. The collected noise data was then used to calculate and predict subjective satisfaction based on a quiet area sound quality assessment model, yielding the final sound environmental quality assessment level. The comparison data between the predicted satisfaction values ​​obtained from the collected noise samples and the on-site satisfaction test results is shown in the table below.

[0072] Table 1 Comparison of Satisfaction Predictions

[0073]

[0074]

[0075] The results above show that the satisfaction level calculated using the prediction function model differs from the subjective satisfaction level obtained from the on-site survey by 9%. This indicates that the method for predicting the subjective satisfaction level of residents in this quiet area using a subjective satisfaction-based assessment of the acoustic environment quality is relatively accurate.

[0076] Example 2

[0077] Embodiment 2 of the present invention proposes an environmental acoustic environment quality assessment system based on subjective satisfaction, implemented based on the method of Embodiment 1, including:

[0078] The acquisition module is used to collect noise audio samples of the area to be evaluated at different time periods;

[0079] The extraction module is used to extract the acoustic feature parameters of noise audio.

[0080] The score prediction module is used to select acoustic feature parameters that are highly correlated with subjective satisfaction, and substitute them into the pre-established sound quality evaluation prediction model to obtain the predicted value of subjective satisfaction score.

[0081] The evaluation module is used to evaluate the acoustic environment quality of the area to be evaluated based on the predicted subjective satisfaction scores.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the embodiments, those skilled in the art should understand that modifications or equivalent substitutions to the technical solutions of the present invention do not depart from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for evaluating acoustic environmental quality of environmental functional areas based on subjective satisfaction, comprising: Step 1: collecting acoustic environment audio samples of the area to be evaluated in different time periods; Step 2: extracting acoustic feature parameters of the acoustic environment audio; Step 3: selecting acoustic feature parameters that have great correlation with subjective satisfaction, substituting them into a pre-established sound quality evaluation prediction model to obtain a predicted value of the subjective satisfaction score; Step 4: evaluating the acoustic environmental quality of the area to be evaluated according to the predicted value of the subjective satisfaction score.

2. The method for evaluating the acoustic environment quality of functional zones based on subjective satisfaction as described in claim 1, characterized in that, Before step 1, the method further comprises: setting acoustic environment collection points in the area to be evaluated according to factors including topography and geomorphology, functional division and potential sound source distribution of the area to be evaluated.

3. The method for evaluating the acoustic environment quality of functional zones based on subjective satisfaction as described in claim 1, characterized in that, The collection duration of the acoustic environment audio samples in said step 1 is no less than 1 minute.

4. The method for evaluating the acoustic environment quality of functional zones based on subjective satisfaction as described in claim 1, characterized in that, The acoustic feature parameters of acoustic environment audio in said step 2 comprise: basic time-domain parameters, frequency-domain parameters, statistical acoustic parameters and psychoacoustic objective parameters; wherein, The basic time-domain parameters include: equivalent continuous A-weighted sound level L. Aeq Equivalent continuous sound level L eq and maximum sound level L max ; The frequency domain parameters include: the sound level D centered at 250Hz. 250 The sound level D with the noise frequency centered at 315Hz 315 and the sound level D with the noise frequency centered at 500Hz. 500 ; The statistical acoustic parameters include: cumulative 10% sound level (L). 10 50% of the total sound level L 50 90% of the total sound level L 90 The difference between the cumulative 10% sound level and the cumulative 90% sound level (L) 10 -L 90 The difference between the cumulative 50% sound level and the cumulative 90% sound level L 50 -L 90 ; said psychoacoustic objective parameters comprise: fluctuation F, sharpness S, roughness R and loudness Lou.

5. The method for evaluating the acoustic environment quality of functional zones based on subjective satisfaction as described in claim 1, characterized in that, The sound quality evaluation prediction model in said step 3 is: F(Sat)=ε+β1*X1+β2*X2+β3*X3+β4*X4+...+β i *X i Where F(Sat) is the predicted subjective satisfaction score, ε is the error term, and X1 is the equivalent continuous A-weighted sound level L. Aeq X2 represents the sound level centered at 500Hz, and X3 represents the difference between the cumulative 10% and 90% sound levels, L. 10 -L 90 X4 represents the fluctuation degree F, and β1 to β4 are the coefficients corresponding to each acoustic characteristic parameter. i For other relevant acoustic characteristic parameters, β i For X i The corresponding coefficients have subscripts i greater than 4.

6. The method for evaluating the acoustic environment quality of functional zones based on subjective satisfaction as described in claim 5, characterized in that, the method further comprises a process for establishing the sound quality evaluation prediction model, comprising: A large number of ambient sound audio samples were collected. Subjective satisfaction evaluation experiments were conducted on these samples to establish the numerical relationship between the acoustic characteristic parameters of each sound source sample and subjective satisfaction. The acoustic characteristic parameters include: equivalent continuous A-weighted sound level L... Aeq The sound level D with the noise frequency centered at 500Hz 500 The difference between the cumulative 10% sound level and the cumulative 90% sound level L 10 -L 90 The sound quality assessment prediction model was determined by curve fitting, where ε = 5.4929, β1 = -0.1451, β2 = 0.0809, β3 = 0.1714, and β4 = 1.7828.

7. The method for evaluating the acoustic environment quality of functional zones based on subjective satisfaction as described in claim 5, characterized in that, said step 4 comprises: judging according to the predicted subjective satisfaction score F(Sat): when F(Sat)≤1, the sound quality evaluation grade is severely dissatisfied; when 1<F(Sat)≤2, the sound quality evaluation grade is relatively dissatisfied; when 2<F(Sat)≤3, the sound quality evaluation grade is slightly dissatisfied; when 3<F(Sat)≤4, the sound quality evaluation grade is average; when 4<F(Sat)≤5, the sound quality evaluation grade is slightly satisfied; when 5<F(Sat)≤6, the sound quality evaluation grade is relatively satisfied; when 6<F(Sat)≤7, the sound quality evaluation grade is very satisfied; when F(Sat)>5, the area to be evaluated is a quiet area, otherwise it is a non-quiet area.

8. A sound environment quality evaluation system for environmental functional zones based on subjective satisfaction, characterized in that, comprising: a collection module, configured to collect acoustic environment audio samples of the area to be evaluated in different time periods; an extraction module, configured to extract acoustic feature parameters of the acoustic environment audio; a score prediction module, configured to select acoustic feature parameters that have great correlation with subjective satisfaction, substitute them into a pre-established sound quality evaluation prediction model to obtain a predicted value of the subjective satisfaction score; and an evaluation module, configured to evaluate the acoustic environmental quality of the area to be evaluated according to the predicted value of the subjective satisfaction score.

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