CN110751959A - Method for evaluating noise discomfort degree of automobile - Google Patents
Method for evaluating noise discomfort degree of automobile Download PDFInfo
- Publication number
- CN110751959A CN110751959A CN201810817073.8A CN201810817073A CN110751959A CN 110751959 A CN110751959 A CN 110751959A CN 201810817073 A CN201810817073 A CN 201810817073A CN 110751959 A CN110751959 A CN 110751959A
- Authority
- CN
- China
- Prior art keywords
- noise
- discomfort
- degree
- automobile
- noise sample
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 17
- 230000001419 dependent effect Effects 0.000 claims abstract description 6
- 238000012886 linear function Methods 0.000 claims abstract description 5
- 230000002194 synthesizing effect Effects 0.000 claims abstract description 4
- 238000011156 evaluation Methods 0.000 claims description 21
- 238000004364 calculation method Methods 0.000 claims description 7
- 238000012417 linear regression Methods 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 4
- 230000005236 sound signal Effects 0.000 claims description 2
- 241000282414 Homo sapiens Species 0.000 description 3
- 238000012935 Averaging Methods 0.000 description 2
- 238000001135 Friedman test Methods 0.000 description 2
- 239000010426 asphalt Substances 0.000 description 2
- 239000004568 cement Substances 0.000 description 2
- 230000005284 excitation Effects 0.000 description 2
- 206010013954 Dysphoria Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 210000005069 ears Anatomy 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000000284 resting effect Effects 0.000 description 1
- 239000004576 sand Substances 0.000 description 1
- 230000013707 sensory perception of sound Effects 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 239000004575 stone Substances 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
- G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Signal Processing (AREA)
- Health & Medical Sciences (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Human Computer Interaction (AREA)
- Acoustics & Sound (AREA)
- Multimedia (AREA)
- General Physics & Mathematics (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
Abstract
The invention discloses a method for evaluating noise discomfort of an automobile. The method comprises the following steps: reading a noise sample signal of an automobile to be evaluated, and calculating the loudness, sharpness, roughness and speech definition of each noise sample; calculating the uncomfortable level of each noise sample according to an uncomfortable level-sound model, wherein the model is a linear function taking loudness, sharpness, roughness and speech definition as independent variables and the uncomfortable level as a dependent variable; and synthesizing the discomfort degree of the noise sample to obtain the comprehensive discomfort degree of the automobile noise to be evaluated. According to the method and the device, the discomfort degree of the noise sample is calculated according to the discomfort degree-sound model, so that the feeling of the passengers in the automobile on the noise can be reflected more truly, and the problem of low accuracy in the prior art that the discomfort degree of the automobile noise is evaluated only according to the sound level of the noise sample A is solved.
Description
Technical Field
The invention belongs to the technical field of automobile noise evaluation, and particularly relates to a noise discomfort degree evaluation method.
Background
The noise is a sound which prevents people from normally resting, learning and working, and interferes with a sound to be heard. The sources of noise are many, such as car sounds on the street, machine sounds at construction sites, and loud sounds from neighboring televisions.
Automotive noise is a major source of noise pollution. The car noise can cause various uncomfortable feelings such as psychological dysphoria of people. In response to comfort, y.huang and m.j.griffin propose that noise and vertical whole body vibration act separately and simultaneously to cause uncomfortable feeling, i.e., discomfort, to a person, and can be used to describe subjective feeling of how much discomfort the vibration and noise act separately or together cause to the person.
The prior art generally adopts the A sound level of a noise sample to evaluate the discomfort degree of the automobile noise. The sound level A is the total sound pressure level of the noise obtained by correcting the sound pressure levels of the sounds with different frequencies through weighting A and then carrying out superposition calculation. For broadband noise with relatively stable frequency spectrum, the A sound level can better reflect subjective feeling. But even with the same a sound level, the noise level may vary from road condition to road condition, causing discomfort to the person. The analysis by Friedman test shows that: the same vehicle runs under different road conditions, and the noise in the vehicle under different road conditions causes the discomfort degree generated by people to have difference under the condition that the sound level of the noise A in the vehicle is the same; a plurality of vehicles run on the same road condition, and the uncomfortable level of noise in each vehicle, which causes people, is different under the condition that the sound level of the noise A in the vehicle is the same. Therefore, in the prior art, the discomfort degree of the automobile noise is evaluated only according to the level of the sound level A, and the evaluation result has large deviation.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a noise discomfort degree evaluation method, which considers the influence of psychoacoustic parameters of a sound sample, namely loudness, sharpness, roughness and speech definition, on discomfort degree, so that the noise comfort degree evaluation method is more objective and reasonable, and the evaluation result is more accurate.
In order to achieve the purpose, the invention adopts the following technical scheme:
an automobile noise discomfort degree evaluation method comprises the following steps:
step 1, reading noise sample signals of an automobile to be evaluated, and calculating the loudness, sharpness, roughness and voice definition of each noise sample signal;
step 2, calculating the uncomfortable degree of each noise sample according to the calculation result of the step 1 and an uncomfortable degree-sound model, wherein the model is a linear function taking loudness, sharpness, roughness and speech definition as independent variables and taking the uncomfortable degree as a dependent variable;
and step 3, synthesizing the discomfort degree of the noise sample to obtain the comprehensive discomfort degree of the automobile noise to be evaluated.
Compared with the prior art, the invention has the following beneficial effects:
the method comprises the steps of reading noise sample signals of an automobile to be evaluated, calculating the loudness, the sharpness, the roughness and the speech definition of each noise sample signal, and calculating the discomfort level of each noise sample according to a discomfort level-sound model, wherein the model is a linear function which takes the loudness, the sharpness, the roughness and the speech definition as independent variables and takes the discomfort level as a dependent variable, and the discomfort level of the noise samples is integrated to obtain the comprehensive discomfort level of the automobile noise to be evaluated, so that the evaluation of the discomfort level of the automobile noise based on the sound quality is realized. According to the method and the device, the discomfort degree of the noise sample is calculated according to the discomfort degree-sound model, so that the feeling of the passengers in the automobile on the noise can be reflected more truly, and the problem of low accuracy in the prior art that the discomfort degree of the automobile noise is evaluated only according to the sound level of the noise sample A is solved.
Drawings
Fig. 1 is a flowchart of a method for evaluating noise discomfort of an automobile according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The flow chart of the method for evaluating the noise discomfort degree of the automobile in the embodiment of the invention is shown in fig. 1, and the method comprises the following steps:
s101, reading noise sample signals of an automobile to be evaluated, and calculating the loudness, sharpness, roughness and voice definition of each noise sample signal;
this step is used to calculate the psychoacoustic parameters of each noise sample-loudness, sharpness, roughness and speech intelligibility. Loudness is also called volume, and reflects the strength of sound sensed by human ears, which is a subjective perception of sound by human beings, and is determined by the amplitude of sound at the receiving site, and is expressed in tone. Sharpness is used to describe the degree of sharpness of a sound in acum. Loudness and sharpness are two important parameters that create a sense of discomfort for a person. Roughness is a parameter describing the degree of sound modulation, in units of asper. Speech intelligibility is a parameter that reflects the degree of intelligibility of speech, and is a percentage. The 4 parameters can all have certain influence on the psychology of the human body, and therefore all belong to psychoacoustic parameters. Of course, these 4 parameters also affect the uncomfortable feeling of people to noise. Among them, the influence of loudness and sharpness is obvious, and the other two parameters are inferior.
S102, calculating the uncomfortable degree of each noise sample according to an uncomfortable degree-sound model according to the calculation result of the S101, wherein the model is a linear function taking loudness, sharpness, roughness and speech definition as independent variables and taking the uncomfortable degree as a dependent variable;
this step is used to calculate the discomfort level of each noise sample based on the 4 psychoacoustic parameters of the noise sample. The calculation method is based on a linear model which takes loudness, sharpness, roughness and speech definition as independent variables and takes uncomfortable degree as a dependent variable, namely an uncomfortable degree-sound model, and 4 psychoacoustic parameters are substituted into the model to obtain the uncomfortable degree of the noise sample. Here, the term "sound" means that the 4 psychoacoustic parameters can also be used to represent sound quality, and thus 4 parameters are replaced by sound. The linear model is an approximate model and is generally obtained by using statistical data and adopting a linear regression method.
S103, synthesizing the discomfort degree of the noise sample to obtain the comprehensive discomfort degree of the automobile noise to be evaluated.
In order to obtain the uncomfortable degree of the whole automobile to be evaluated, the uncomfortable degree of each noise sample obtained in the step S102 is integrated to obtain the integrated uncomfortable degree of the whole automobile. The integration method may be averaging the noise samples for discomfort or weighted averaging. Of course, other possible combinations may be used. It should be noted that the magnitude of the discomfort level obtained in this embodiment can only be used for comparison between different automobiles using the evaluation method of this embodiment, and the influence of the noise of the automobile with a large discomfort level is large, but cannot be compared with automobiles using different evaluation methods, because the magnitude (absolute value) of the discomfort level obtained between different evaluation methods is not uniform.
By performing Friedman test analysis, it was found that: the same vehicle runs under different road conditions, and the noise in the vehicle under different road conditions causes the discomfort degree generated by people to have difference under the condition that the sound level of the noise A in the vehicle is the same; a plurality of vehicles run on the same road condition, and the uncomfortable level of noise in each vehicle, which causes people, is different under the condition that the sound level of the noise A in the vehicle is the same. The method is based on 4 psychoacoustic parameters of loudness, sharpness, roughness and speech definition, the discomfort degree of the noise sample is calculated according to the discomfort degree-sound model, the feeling of the passengers in the automobile to the noise can be reflected more truly, and the problem that in the prior art, the accuracy is not high when the discomfort degree of the automobile noise is evaluated only according to the sound level of the noise sample A is solved.
As an optional embodiment, the noise sample is a sound signal collected from an automobile to be evaluated running at different speeds under different road conditions, the time duration of the noise sample is 5 seconds, and the sound pressure level range is 49-91 dB (A).
In this embodiment, the noise sample is a real noise signal collected from a running vehicle to be evaluated, and the vehicle runs under different road conditions at different speeds. The duration of each noise sample is 5 seconds, and the A sound pressure level ranges from 49 dB (A) to 91dB (A). Different road conditions can be asphalt road, cement road or soil road, etc.; the speed can be selected from 30, 40, 60, 80 km/h and the like.
As an alternative embodiment, the S101 calculates the loudness, sharpness, roughness and speech intelligibility of the noise samples using Artemis software.
This embodiment provides a technical solution for calculating the loudness, sharpness, roughness and speech intelligibility of noise samples. This example calculates the above 4 parameters using Artemis software. The noise sample signal is input and the Artemis software can automatically calculate the 4 parameters. The loudness calculation adopts a Zwicker model, namely a loudness calculation method given in ISO 532B. The method is suitable for a steady sound field. Sharpness is based on the resulting loudness.
As an alternative embodiment, the method of establishing the discomfort level-sound model of S102 includes:
s1021, sequentially playing the sound recordings of noise samples, wherein the noise samples are collected in real time and are generated when automobiles of different vehicle types run under different road conditions, the time length of the noise samples is 5 seconds, and the sound pressure level range is 49-91 dB (A);
s1022, inputting a mark for the discomfort degree generated by each noise sample by an absolute amplitude evaluation method by an evaluator, wherein the mark value is any positive number;
s1023, standardizing the scoring according to a formula (1):
in the formula (1), aijScoring the discomfort level of the jth evaluator on the ith noise sample;
is a pair ofijNormalizing the normalized value; a. thejA median score for the noise sample discomfort of the jth evaluator; i is 1, 2, M is the number of noise samples, j is 1, 2, N is the number of evaluators; k is to make all
Are all greater than a normal number of 0.
To find
The median of (a), which is the discomfort level of the ith noise sample;
s1024, calculating the loudness, sharpness, roughness and speech definition of each noise sample by using Artemis software;
s1025, performing multiple linear regression processing on the discomfort degree of the noise sample obtained in the S1023 and the loudness, sharpness, roughness and speech definition of the noise sample obtained in the S1024 to obtain the discomfort degree-sound model:
in the formula (2), y is the discomfort value of the noise sample, x1、x2、x3And x4Loudness, sharpness, roughness and speech intelligibility, k, respectively, of the noise samples0Is a constant term, k1、k2、k3And k4Are respectively x1、x2、x3And x4The coefficient of (a).
The embodiment provides a specific method for realizing S102, which includes S1021 to S1025.
And S1021, playing the sound recordings of the noise samples in sequence for the evaluator, and scoring the discomfort degree according to the sound recordings. This step is to prepare for the following building of the discomfort level-sound model. Preferably, in order to adapt the model to different road conditions and vehicle types, the collected noise samples include noise generated when vehicles of different vehicle types run under different road conditions, such as 3 vehicle types (A, B, C) and 4 road conditions (asphalt, cement, sand and stone, bump); the a-level of the noise sample is an arithmetic series with a tolerance of 3db (a).
And S1022, scoring the discomfort degree generated by each noise sample by an evaluator by adopting an absolute amplitude evaluation method, and inputting the score into a computer. The absolute amplitude evaluation method does not need reference excitation, and an evaluator can subjectively score the discomfort caused by vibration or noise excitation by any positive number which is considered suitable by the evaluator, for example, the score of a group of noise samples is the same, and the score of A can be 1-10, and the score of B can be 1000-2000.
S1023 is used to derive the discomfort level of each noise sample. The absolute amplitude evaluation method has the advantages that the use is convenient, evaluators can freely score without limitation, but the evaluation method does not have a uniform magnitude, and the evaluation results obtained by the evaluators are inconvenient to compare. For this purpose, S1023 first normalizes the scores of the absolute amplitude evaluation method, transforming the widely spread data into a narrow range. The median of the scores of the discomfort degree generated by the jth evaluator on the noise sample is firstly obtained, and the values obtained by dividing the scores by the median are approximately distributed by taking 1 as the center, namely, one part is greater than or equal to 1, and the other part is less than 1. The score value is divided by the median to compress the original distribution range to some extent. The logarithm of the value after the initial compression is taken, and the distribution range of the scores is further effectively compressed. In order to prevent the logarithm result from generating negative numbers, the preliminarily compressed value is multiplied by a normal number K, so that the product is larger than 1, and the logarithm value is not negative. The normalized values obtained by the above-mentioned processing can be concentrated in a small range. The noise sample discomfort is then obtained by taking the median of the normalized values that all evaluators score the same noise sample.
S1024, calculating the loudness, sharpness, roughness and speech definition of each noise sample by using Artemis software; and S1025, performing multiple linear regression processing on the discomfort degree of the noise sample obtained in the S1023 and the loudness, sharpness, roughness and speech definition of the noise sample obtained in the S1024 to obtain the discomfort degree-sound model.
The above description is only for the purpose of illustrating a few embodiments of the present invention, and should not be taken as limiting the scope of the present invention, in which all equivalent changes, modifications, or equivalent scaling-up or down, etc. made in accordance with the spirit of the present invention should be considered as falling within the scope of the present invention.
Claims (4)
1. An automobile noise discomfort degree evaluation method is characterized by comprising the following steps:
step 1, reading noise sample signals of an automobile to be evaluated, and calculating the loudness, sharpness, roughness and voice definition of each noise sample signal;
step 2, calculating the uncomfortable degree of each noise sample according to the calculation result of the step 1 and an uncomfortable degree-sound model, wherein the model is a linear function taking loudness, sharpness, roughness and speech definition as independent variables and taking the uncomfortable degree as a dependent variable;
and step 3, synthesizing the discomfort degree of the noise sample to obtain the comprehensive discomfort degree of the automobile noise to be evaluated.
2. The method for evaluating noise discomfort of a vehicle according to claim 1, wherein the noise samples are sound signals collected from vehicles to be evaluated traveling at different speeds under different road conditions, the noise samples have a duration of 5 seconds and a sound pressure level ranging from 49 dB (A) to 91dB (A).
3. The automobile noise discomfort level evaluation method according to claim 1, wherein said
Step 1 calculates the loudness, sharpness, roughness and speech intelligibility of the noise samples using Artemis software.
4. The automobile noise discomfort level evaluation method according to any one of claims 1 to 3, wherein the method of establishing the discomfort-level sound model of step 2 includes:
step 2.1, sequentially playing the recordings of noise samples, wherein the noise samples are collected in real time and are generated when automobiles of different vehicle types run under different road conditions, the time length of the noise samples is 5 seconds, and the sound pressure level range is 49-91 dB (A);
step 2.2, inputting a mark for the discomfort degree generated by each noise sample by an evaluator, wherein the mark value is any positive number;
step 2.3, standardizing the scores according to a formula (1):
in the formula (1), aijScoring the discomfort level of the jth evaluator on the ith noise sample;
is a pair ofijNormalizing the normalized value; a. thejA median score for the noise sample discomfort of the jth evaluator; i is 1, 2, M is the number of noise samples, j is 1, 2, N is the number of evaluators; k is to make all
Normal numbers that are both greater than 0;
to find
The median of (a), which is the discomfort level of the ith noise sample;
step 2.4, calculating the loudness, sharpness, roughness and speech definition of each noise sample by using Artemis software;
step 2.5, performing multiple linear regression processing on the uncomfortable degree of the noise sample obtained in the step 2.3 and the loudness, sharpness, roughness and speech definition of the noise sample obtained in the step 2.4 to obtain the uncomfortable degree-sound model:
in the formula (2), y is the discomfort value of the noise sample, x1、x2、x3And x4Loudness, sharpness, roughness and speech intelligibility, k, respectively, of the noise samples0Is a constant term, k1、k2、k3And k4Are respectively x1、x2、x3And x4The coefficient of (a).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810817073.8A CN110751959A (en) | 2018-07-24 | 2018-07-24 | Method for evaluating noise discomfort degree of automobile |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810817073.8A CN110751959A (en) | 2018-07-24 | 2018-07-24 | Method for evaluating noise discomfort degree of automobile |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110751959A true CN110751959A (en) | 2020-02-04 |
Family
ID=69275300
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810817073.8A Pending CN110751959A (en) | 2018-07-24 | 2018-07-24 | Method for evaluating noise discomfort degree of automobile |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110751959A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109668626A (en) * | 2018-12-25 | 2019-04-23 | 东莞材料基因高等理工研究院 | A kind of sound quality evaluation method based on human-computer interaction interface |
| CN112259125A (en) * | 2020-10-23 | 2021-01-22 | 江苏理工学院 | Noise-based comfort evaluation method, system, equipment and storage medium |
| CN112729859A (en) * | 2020-12-02 | 2021-04-30 | 万向钱潮（上海）汽车系统有限公司 | Electronic parking noise quality evaluation system and method |
| CN113125000A (en) * | 2021-04-20 | 2021-07-16 | 中国汽车工程研究院股份有限公司 | Abnormal sound grade judging method for in-vehicle air conditioning system |
| CN113486448A (en) * | 2021-07-19 | 2021-10-08 | 上汽通用五菱汽车股份有限公司 | Method for evaluating transmission squeal based on masking effect |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103329136A (en) * | 2010-11-26 | 2013-09-25 | 具兴燮 | Representative-value calculating device and method |
| CN105021275A (en) * | 2015-07-15 | 2015-11-04 | 吉林大学 | Time domain dynamic characteristic analysis-based intra-vehicle sound quality prediction method |
| JP2016218518A (en) * | 2015-05-14 | 2016-12-22 | 富士通株式会社 | Evaluation support device, evaluation support program, and evaluation support system |
-
2018
- 2018-07-24 CN CN201810817073.8A patent/CN110751959A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103329136A (en) * | 2010-11-26 | 2013-09-25 | 具兴燮 | Representative-value calculating device and method |
| JP2016218518A (en) * | 2015-05-14 | 2016-12-22 | 富士通株式会社 | Evaluation support device, evaluation support program, and evaluation support system |
| CN105021275A (en) * | 2015-07-15 | 2015-11-04 | 吉林大学 | Time domain dynamic characteristic analysis-based intra-vehicle sound quality prediction method |
Non-Patent Citations (4)
| Title |
|---|
| 张志辉等: "线性学科标准化方法的效果优化及其对科研评价结果的影响――以39所"985工程"大学论文质量排名为例", 《情报学报》, no. 03, 24 March 2015 (2015-03-24) * |
| 林昊 等: "《简明生物信息学》", 电子科技大学出版社, pages: 126 - 127 * |
| 黄灿银等: "基于声品质评价模型的车内噪声优化研究", 《柴油机设计与制造》 * |
| 黄灿银等: "基于声品质评价模型的车内噪声优化研究", 《柴油机设计与制造》, vol. 18, no. 03, 25 September 2012 (2012-09-25), pages 25 - 30 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109668626A (en) * | 2018-12-25 | 2019-04-23 | 东莞材料基因高等理工研究院 | A kind of sound quality evaluation method based on human-computer interaction interface |
| CN112259125A (en) * | 2020-10-23 | 2021-01-22 | 江苏理工学院 | Noise-based comfort evaluation method, system, equipment and storage medium |
| CN112259125B (en) * | 2020-10-23 | 2023-06-16 | 江苏理工学院 | Noise-based comfort evaluation method, system, device and storable medium |
| CN112729859A (en) * | 2020-12-02 | 2021-04-30 | 万向钱潮（上海）汽车系统有限公司 | Electronic parking noise quality evaluation system and method |
| CN113125000A (en) * | 2021-04-20 | 2021-07-16 | 中国汽车工程研究院股份有限公司 | Abnormal sound grade judging method for in-vehicle air conditioning system |
| CN113486448A (en) * | 2021-07-19 | 2021-10-08 | 上汽通用五菱汽车股份有限公司 | Method for evaluating transmission squeal based on masking effect |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110751959A (en) | Method for evaluating noise discomfort degree of automobile | |
| Wang et al. | Sound-quality prediction for nonstationary vehicle interior noise based on wavelet pre-processing neural network model | |
| Wang et al. | A sound quality model for objective synthesis evaluation of vehicle interior noise based on artificial neural network | |
| Wang et al. | Roughness modelling based on human auditory perception for sound quality evaluation of vehicle interior noise | |
| CN103471709A (en) | Method for predicting noise quality of noise inside passenger vehicle | |
| Swart et al. | The subjective dimensions of sound quality of standard production electric vehicles | |
| Klein et al. | Spectral and modulation indices for annoyance-relevant features of urban road single-vehicle pass-by noises | |
| Kwon et al. | Model of psychoacoustic sportiness for vehicle interior sound: Excluding loudness | |
| CN101672690A (en) | Method for objectively and quantifiably evaluating noise fret degree in vehicle based on auditory model | |
| CN102475554B (en) | Method for guiding interior sound package by utilizing sound quality | |
| DE102020114146A1 (en) | SPEAKER IMAGE OF A MICROPHONE FOR WIND DETECTION | |
| KR101330923B1 (en) | Method for sound quality analysis of vehicle noise using gammatone filter and apparatus thereof | |
| Lee et al. | Objective evaluation of interior noise booming in a passenger car based on sound metrics and artificial neural networks | |
| CN110307895A (en) | Allophone decision maker and allophone determination method | |
| Hoffmann et al. | Bass enhancement settings in portable devices based on music genre recognition | |
| Lemaitre et al. | A psychoacoustical study of wind buffeting noise | |
| CN112131662A (en) | Passenger car wind noise subjective evaluation objective quantification method | |
| Kim et al. | Sound quality evaluation of the impact noise induced by road courses having an impact bar and speed bumps in a passenger car | |
| CN113343384B (en) | Sound quality subjective and objective evaluation method under variable rotating speed working condition of transmission | |
| CN105869656A (en) | Speech signal definition determining method and device | |
| CN107093432B (en) | Voice quality evaluation system for communication system | |
| Torija et al. | Subjective dominance as a basis for selecting frequency weightings | |
| CN115307721A (en) | Method, device and equipment for evaluating quality of automobile acceleration sound and storage medium | |
| KR100354969B1 (en) | Method for assessing sound quality of automobile interior noise | |
| JPH11142231A (en) | Noise analyzer |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200204 |