CN113758713A - Adaptive rough acoustic frequency band identification method - Google Patents

Adaptive rough acoustic frequency band identification method Download PDF

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CN113758713A
CN113758713A CN202110909538.4A CN202110909538A CN113758713A CN 113758713 A CN113758713 A CN 113758713A CN 202110909538 A CN202110909538 A CN 202110909538A CN 113758713 A CN113758713 A CN 113758713A
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frequency band
rotating speed
modulation
matrix
critical
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CN113758713B (en
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罗乐
杨少波
杨金才
曾庆强
蔡晶
王蕾
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Chongqing Changan Automobile Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M15/00—Testing of engines
    • G01M15/04—Testing internal-combustion engines
    • G01M15/05—Testing internal-combustion engines by combined monitoring of two or more different engine parameters
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M17/00—Testing of vehicles
    • G01M17/007—Wheeled or endless-tracked vehicles
    • 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
    • G10L25/18—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters the extracted parameters being spectral information of each sub-band
    • 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
    • G10L25/27—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the analysis technique
    • 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
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    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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Abstract

The invention discloses a rough acoustic frequency band self-adaptive identification method, which comprises the following steps: s1, synchronously acquiring an engine rotating speed signal and an automobile or engine noise signal, and respectively performing band-pass filtering on the noise signal to obtain sound signal matrixes BP with different critical frequency bands; s2, respectively dividing the BP according to the self-defined rotating speed increment to obtain sound signal matrixes T in different rotating speed rangesn(ii) a S3, respectively aligning sound signal matrixes TnPerforming Hilbert transform, and calculating to obtain corresponding envelope matrix En(ii) a S4, respectively aligning envelope line matrixes EnFourier transform is carried out, and different modulation frequencies are obtained through calculationModulation depth matrix D ofn(ii) a S5, determining a modulation depth matrix O of the 0.5 order modulation order under different rotating speeds based on a peak value holding principle according to the corresponding relation between the modulation frequency and the modulation ordern(ii) a And S6, drawing a time or rotating speed-critical frequency band cloud picture of 0.5-order modulation depth, and identifying the characteristic frequency band of the rough sound of the automobile or the engine. The method can quickly identify the characteristic frequency band of the rough sound of the automobile or the engine.

Description

Adaptive rough acoustic frequency band identification method
Technical Field
The invention belongs to the technical field of automobile NVH (noise vibration and harshness), and particularly relates to a rough acoustic frequency band self-adaptive identification method.
Background
Reciprocating engines are typical rotating machines and operating noise has a significant order modulation characteristic. Relevant research shows that the rough complaint of the automobile or the engine is mainly related to the strength of the 0.5-order modulation of the engine, the modulation of the proper frequency band can increase the sound movement feeling and bring pleasant driving experience to people, but if the 0.5-order modulation phenomenon is too prominent or appears in an improper frequency band, the subjective feeling of high annoyance can be caused. According to the analysis experience of the past engineering case, the modulation phenomenon is generally distributed in a plurality of frequency bands of noise signals of the automobile or the engine discretely, but the modulation depths are obviously different to different degrees. Therefore, how to quickly identify the characteristic frequency band of the rough sound and further form strong correlation with subjective sound quality complaints of human ears is a key for analyzing the rough sound generation mechanism and is also an important reference basis for a subsequent optimization scheme.
Patent document CN112326267A discloses a method and system for determining an accelerated coarse sound effect result, which determine an initial noise frequency corresponding to a broadband resonance band through an accelerated noise cloud diagram, and then determine a coarse sound frequency band together with a filter playback and a vibration frequency of a suspended passive end. The method has the defects that a plurality of vibration and noise measuring points need to be synchronously arranged, the flow of testing and subsequent analysis is complicated, multilayer subjective screening needs to be carried out when a rough sound frequency band is confirmed, and the method is high in uncertainty and poor in self-adaptability. In summary, the existing analysis methods for rough sound of automobiles or engines are few and have obvious defects, and the quick identification of rough sound frequency bands cannot be effectively guided.
Therefore, it is necessary to develop a rough acoustic band adaptive identification method.
Disclosure of Invention
The invention aims to provide a rough sound frequency band self-adaptive identification method, which can be used for quickly identifying the characteristic frequency band of rough sound of an automobile or an engine through transverse comparison by drawing a time (or rotating speed) -critical frequency band cloud chart with 0.5-order modulation depth.
The invention relates to a rough acoustic frequency band self-adaptive identification method, which comprises the following steps:
step 1, synchronously acquiring an engine rotating speed signal and an automobile or engine noise signal, and respectively carrying out band-pass filtering on the noise signal according to a critical frequency band division principle to obtain a sound signal matrix BP of different critical frequency bands;
step 2, respectively dividing BP according to self-defined rotating speed increment to obtain sound signal matrixes T in different rotating speed rangesn；
Step 3, respectively aligning the sound signal matrixes TnPerforming Hilbert transform, and calculating to obtain corresponding envelope matrix En；
Step 4, respectively aligning envelope line matrixes EnFourier transformation is carried out, and a modulation depth matrix D under different modulation frequencies is obtained through calculationn；
Step 5, determining a modulation depth matrix O of the modulation order of 0.5 order under different rotating speeds based on the peak value holding principle according to the corresponding relation between the modulation frequency and the modulation ordern；
And 6, drawing a time or rotating speed-critical frequency band cloud picture of 0.5-order modulation depth, and identifying the characteristic frequency band of the rough sound of the automobile or the engine through transverse comparison.
Optionally, step 1 specifically includes:
the method comprises the following steps of collecting an engine rotating speed signal and an automobile or engine noise signal by adopting the same time sampling rate, respectively carrying out band-pass filtering on the noise signals, and determining the relation between the center frequency and the bandwidth by the following formula:
BWn＝(25+75×(1+1.4×(fc/1000)2)0.69)×ΔBark；
wherein, BWnIs the critical band bandwidth, n is the number of critical bands, Δ Bark is the critical band increment, fcCritical band center frequency;
and obtaining sound signal matrixes BP with different critical frequency bands.
Optionally, the step 2 specifically includes:
determining the central point of each data block according to the initial rotating speed and the rotating speed increment, then determining the start point and the stop point of each data block according to the time sampling frequency and the frequency resolution, and obtaining the sound signal matrix T in different rotating speed rangesn。
Optionally, step 3 specifically includes:
for TnHilbert transformation is performed in sections, and an absolute value is taken to obtain an envelope matrix En：
En＝|Hilbert[Tn]|。
Optionally, the step 4 specifically includes:
first, for EnAnd carrying out FFT (fast Fourier transform) on the segments, and taking an absolute value to obtain an amplitude spectrum:
Figure BDA0003203001200000021
wherein, FnIs a corresponding spectrum matrix, A0Is an amplitude matrix corresponding to 0Hz, AiFor amplitude matrices corresponding to non-zero frequencies, fiRepresenting the frequency of analysis, t represents time,
Figure BDA0003203001200000022
represents the phase;
then, according to A0And AiCalculating a modulation depth matrix Dn：
Figure BDA0003203001200000023
Optionally, the step 5 specifically includes:
firstly, determining the upper limit and the lower limit of a 0.5-order modulation frequency according to a self-defined order width;
then, a modulation depth matrix O of the 0.5 order modulation order at different rotation speeds is determined based on the principle of peak holding in the upper and lower limit frequency rangesn。
Optionally, step 6 specifically includes:
and comparing time or rotating speed-critical frequency band cloud pictures of 0.5 order modulation depths of different noise signals, and quickly identifying to obtain the characteristic frequency band of the rough sound of the automobile or the engine.
The invention has the following advantages: the method has self-adaptability, adopts a uniform critical frequency band division principle to carry out filtering processing, accords with the nonlinear auditory characteristic of human ears, and avoids the uncertainty of selecting a filtering frequency band according to a spectrum cloud picture. According to the invention, 0.5-order modulation depth cloud maps of different noise signals are transversely compared, the characteristic frequency band of the rough sound of the automobile or the engine can be rapidly identified, so that the working process of testing and analyzing is greatly simplified, and meanwhile, a clear guidance direction is provided for the engineering improvement scheme of the rough sound.
Drawings
FIG. 1 is a schematic flow chart of the present embodiment;
FIG. 2 is a schematic comparison diagram of 0.5-order modulated depth clouds of a noise signal near the inner ear of a car A;
fig. 3 is a comparison diagram of 0.5 order modulation depth cloud maps of a near-field noise signal of a certain engine B.
Detailed Description
The invention will be further explained with reference to the drawings.
In this embodiment, a rough acoustic frequency band adaptive identification method includes the following steps:
(1) synchronously acquiring an engine rotating speed signal and an automobile or engine noise signal, and respectively performing band-pass filtering on the noise signal according to a critical frequency band division principle to obtain a sound signal matrix BP of different critical frequency bands, specifically:
the method comprises the following steps of collecting an engine rotating speed signal and an automobile or engine noise signal by adopting the same time sampling rate, and respectively carrying out band-pass filtering on the noise signal, wherein the relation between the central frequency and the bandwidth is determined by the following formula:
BWn＝(25+75×(1+1.4×(fc/1000)2)0.69)×ΔBark；
wherein, BWnIs the critical band bandwidth, n is the number of critical bands, Δ Bark is the critical band increment, fcIs the critical band center frequency.
The finally divided sound signal matrix is BP.
In this example, the number n of critical bands is 47, the critical band bandwidth Δ Bark is 0.5, and the upper and lower limit frequencies corresponding to the critical bands are shown in table 1.
TABLE 1
Figure BDA0003203001200000041
(2) Respectively dividing BP according to self-defined rotating speed increment to obtain sound signal matrixes T in different rotating speed rangesnThe method specifically comprises the following steps:
according to the initial speed of rotation R0And the rotation speed increment delta R determines the central point N (R) of each data blockm)midThen, the start and stop points of each data block are determined according to the time sampling rate fs and the frequency resolution df:
N(Rm)1＝N(Rm)mid-fs/2df；N(Rm)end＝N(Rm)mid+fs/2df.
wherein m is 1,2, …, mR，mRTotal number of data blocks in the rotation speed sequence, wherein N (R)m)1Is the start of the mth data block, N (R)m)endIs the end point of the mth data block.
The finally divided sound signal matrix is Tn。
(3) Respectively to the sound signal matrix TnPerforming Hilbert transform, and calculating to obtain corresponding envelope matrix EnThe method specifically comprises the following steps:
for TnHilbert transform is carried out on the segments, and absolute values are takenObtain an envelope matrix En：
En＝|Hilbert[Tn]|。
(4) Respectively to envelope matrix EnFourier Transform (FFT) is carried out, and a modulation depth matrix D under different modulation frequencies is obtained through calculationnThe method specifically comprises the following steps:
first, for EnAnd carrying out FFT (fast Fourier transform) on the segments, and taking an absolute value to obtain an amplitude spectrum:
Figure BDA0003203001200000042
wherein, FnIs a corresponding spectrum matrix, A0Is an amplitude matrix (i.e. DC component matrix) corresponding to 0Hz, AiAmplitude matrix (i.e. AC component matrix) corresponding to non-zero frequency fiRepresenting the frequency of analysis, t represents time,
Figure BDA0003203001200000043
indicating the phase.
Then, according to A0And AiCalculating a modulation depth matrix Dn：
Figure BDA0003203001200000051
(5) Determining a modulation depth matrix O of the modulation order of 0.5 order under different rotating speeds based on the peak value holding principle according to the corresponding relation between the modulation frequency and the modulation ordernThe method specifically comprises the following steps:
firstly, according to the self-defined order width OwDetermining an upper limit f of the modulation frequency of order 0.5muLower limit of fmd：
Figure BDA0003203001200000052
Wherein R is the rotating speed.
Then, the modulation depth of the 0.5 order modulation order at different rotation speeds is determined based on the peak hold principle:
On＝max[Dn],fm∈[fmd,fmu]。
(6) drawing a time (or rotating speed) -critical frequency band cloud chart of 0.5-order modulation depth, and identifying the characteristic frequency band of the rough sound of the automobile or the engine through transverse comparison, specifically:
and comparing the time (or rotating speed) -critical frequency band cloud pictures of 0.5 order modulation depths of different noise signals, and identifying the characteristic frequency band of the rough sound of the automobile or the engine.
In summary, the complete algorithm flow is shown in fig. 1.
Fig. 2 shows a cloud chart of the 0.5 order modulation depth of a noise signal near the ear in a car a along with time and critical frequency band, and the signal corresponding to fig. 2(a) is subjectively evaluated to have rough sound characteristics, while the signal corresponding to fig. 2(b) has no rough sound characteristics. The characteristic critical frequency band of the rough sound is determined to be 3.5-4.5 bark through calculation and lateral comparison, namely the middle and low frequency band of 300-450 Hz.
Fig. 3 shows a cloud chart of the 0.5 order modulation depth of a near-field noise signal of an engine B with time and critical frequency bands, and the signal corresponding to fig. 3(a) is subjectively evaluated to have coarse acoustic features, while the signal corresponding to fig. 3(B) has no coarse acoustic features. The characteristic critical frequency band of the rough sound is determined to be 10-17.5 bark, namely 1170-4000 Hz middle and high frequency band by calculation and transverse comparison.
In FIGS. 2 and 3, Time is Time, Critical Bank is Critical band, and Modulation depth is Modulation depth
According to the adaptive identification method of the rough sound characteristic frequency band, provided by the invention, the working flow of testing and analysis is greatly simplified, and meanwhile, a clear guidance direction is provided for efficiently formulating the engineering improvement scheme of the rough sound.

Claims (7)

1. A rough acoustic frequency band self-adaptive identification method is characterized by comprising the following steps:
step 1, synchronously acquiring an engine rotating speed signal and an automobile or engine noise signal, and respectively carrying out band-pass filtering on the noise signal according to a critical frequency band division principle to obtain a sound signal matrix BP of different critical frequency bands;
step 2, respectively dividing BP according to self-defined rotating speed increment to obtain sound signal matrixes T in different rotating speed rangesn；
Step 3, respectively aligning the sound signal matrixes TnPerforming Hilbert transform, and calculating to obtain corresponding envelope matrix En；
Step 4, respectively aligning envelope line matrixes EnFourier transformation is carried out, and a modulation depth matrix D under different modulation frequencies is obtained through calculationn；
Step 5, determining a modulation depth matrix O of the modulation order of 0.5 order under different rotating speeds based on the peak value holding principle according to the corresponding relation between the modulation frequency and the modulation ordern；
And 6, drawing a time or rotating speed-critical frequency band cloud picture of 0.5-order modulation depth, and identifying the characteristic frequency band of the rough sound of the automobile or the engine through transverse comparison.
2. The adaptive rough acoustic frequency band recognition method according to claim 1, wherein: the step 1 specifically comprises the following steps:
the method comprises the following steps of collecting an engine rotating speed signal and an automobile or engine noise signal by adopting the same time sampling rate, respectively carrying out band-pass filtering on the noise signals, and determining the relation between the center frequency and the bandwidth by the following formula:
BWn＝(25+75×(1+1.4×(fc/1000)2)0.69)×ΔBark；
wherein, BWnIs the critical band bandwidth, n is the number of critical bands, Δ Bark is the critical band increment, fcCritical band center frequency;
and obtaining sound signal matrixes BP with different critical frequency bands.
3. The adaptive rough acoustic frequency band recognition method according to claim 2, wherein the step 2 specifically comprises:
determining the central point of each data block according to the initial rotating speed and the rotating speed incrementThen, determining the start and stop points of each data block according to the time sampling frequency and the frequency resolution to obtain the sound signal matrix T with different rotating speed rangesn。
4. The adaptive rough acoustic band recognition method of claim 3, wherein: the step 3 specifically comprises the following steps:
for TnHilbert transformation is performed in sections, and an absolute value is taken to obtain an envelope matrix En：
En＝|Hilbert[Tn]|。
5. The adaptive rough acoustic frequency band recognition method according to claim 4, wherein the step 4 specifically comprises:
first, for EnAnd carrying out FFT (fast Fourier transform) on the segments, and taking an absolute value to obtain an amplitude spectrum:
Figure FDA0003203001190000021
wherein, FnIs a corresponding spectrum matrix, A0Is an amplitude matrix corresponding to 0Hz, AiFor amplitude matrices corresponding to non-zero frequencies, fiRepresenting the frequency of analysis, t represents time,
Figure FDA0003203001190000022
represents the phase;
then, according to A0And AiCalculating a modulation depth matrix Dn：
Figure FDA0003203001190000023
6. The adaptive rough acoustic frequency band recognition method according to claim 5, wherein the step 5 specifically comprises:
firstly, determining the upper limit and the lower limit of a 0.5-order modulation frequency according to a self-defined order width;
then, a modulation depth matrix O of the 0.5 order modulation order at different rotation speeds is determined based on the principle of peak holding in the upper and lower limit frequency rangesn。
7. The adaptive rough acoustic frequency band recognition method according to claim 6, wherein the step 6 specifically comprises: and comparing time or rotating speed-critical frequency band cloud pictures of 0.5 order modulation depths of different noise signals, and quickly identifying to obtain the characteristic frequency band of the rough sound of the automobile or the engine.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114441177A (en) * 2022-01-30 2022-05-06 重庆长安汽车股份有限公司 Method, system and equipment for quantitatively evaluating engine noise based on signal modulation
CN115077690A (en) * 2022-06-27 2022-09-20 重庆长安汽车股份有限公司 Method for evaluating periodic pulsation noise of internal combustion engine
CN115795899A (en) * 2022-12-12 2023-03-14 博格华纳汽车零部件（武汉）有限公司 New energy electric vehicle squeaking noise evaluation method

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH1123357A (en) * 1997-06-27 1999-01-29 Bridgestone Corp Evaluation method for noise of tire
JP2018096741A (en) * 2016-12-09 2018-06-21 大成建設株式会社 Architectural structure wind noise evaluation method
CN109194306A (en) * 2018-08-28 2019-01-11 重庆长安汽车股份有限公司 A kind of method and device quantifying automobile noise modulation problems
CN109596354A (en) * 2018-12-21 2019-04-09 电子科技大学 Band-pass filtering method based on the identification of adaptive resonance frequency band
CN109781245A (en) * 2019-01-15 2019-05-21 江铃汽车股份有限公司 A kind of method for objectively evaluating of diesel engine impulsive noise
CN109920439A (en) * 2019-03-14 2019-06-21 西安交通大学 The variable-speed motor that subtracts based on tone energy and human ear frequency selectivity is uttered long and high-pitched sounds evaluation method
CN110803102A (en) * 2019-08-13 2020-02-18 中国第一汽车股份有限公司 In-vehicle engine order sound analysis method and electric vehicle active sound production system
CN111210801A (en) * 2020-02-25 2020-05-29 北京绿创声学工程股份有限公司 Tonal noise compensation elimination method and system
CN112304632A (en) * 2020-07-08 2021-02-02 重庆长安汽车股份有限公司 Transient modulation evaluation method for describing human ear perception
CN113053351A (en) * 2021-03-14 2021-06-29 西北工业大学 Method for synthesizing noise in airplane cabin based on auditory perception

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH1123357A (en) * 1997-06-27 1999-01-29 Bridgestone Corp Evaluation method for noise of tire
JP2018096741A (en) * 2016-12-09 2018-06-21 大成建設株式会社 Architectural structure wind noise evaluation method
CN109194306A (en) * 2018-08-28 2019-01-11 重庆长安汽车股份有限公司 A kind of method and device quantifying automobile noise modulation problems
CN109596354A (en) * 2018-12-21 2019-04-09 电子科技大学 Band-pass filtering method based on the identification of adaptive resonance frequency band
CN109781245A (en) * 2019-01-15 2019-05-21 江铃汽车股份有限公司 A kind of method for objectively evaluating of diesel engine impulsive noise
CN109920439A (en) * 2019-03-14 2019-06-21 西安交通大学 The variable-speed motor that subtracts based on tone energy and human ear frequency selectivity is uttered long and high-pitched sounds evaluation method
CN110803102A (en) * 2019-08-13 2020-02-18 中国第一汽车股份有限公司 In-vehicle engine order sound analysis method and electric vehicle active sound production system
CN111210801A (en) * 2020-02-25 2020-05-29 北京绿创声学工程股份有限公司 Tonal noise compensation elimination method and system
CN112304632A (en) * 2020-07-08 2021-02-02 重庆长安汽车股份有限公司 Transient modulation evaluation method for describing human ear perception
CN113053351A (en) * 2021-03-14 2021-06-29 西北工业大学 Method for synthesizing noise in airplane cabin based on auditory perception

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
肖生浩;刘志恩;颜伏伍;郑灏;: "基于阶次设计的汽车排气噪声品质运动感调校", 华中科技大学学报(自然科学版), no. 10, pages 58 - 63 *
邱森: "加速工况车内声品质评价及优化研究", 中国博士学位论文全文数据库工程科技Ⅱ辑, vol. 1, no. 01, pages 035 - 275 *

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114441177A (en) * 2022-01-30 2022-05-06 重庆长安汽车股份有限公司 Method, system and equipment for quantitatively evaluating engine noise based on signal modulation
CN114441177B (en) * 2022-01-30 2023-07-07 重庆长安汽车股份有限公司 Method, system and equipment for quantitatively evaluating engine noise based on signal modulation
CN115077690A (en) * 2022-06-27 2022-09-20 重庆长安汽车股份有限公司 Method for evaluating periodic pulsation noise of internal combustion engine
CN115077690B (en) * 2022-06-27 2024-04-19 重庆长安汽车股份有限公司 Method for evaluating periodic pulsation noise of internal combustion engine
CN115795899A (en) * 2022-12-12 2023-03-14 博格华纳汽车零部件（武汉）有限公司 New energy electric vehicle squeaking noise evaluation method
CN115795899B (en) * 2022-12-12 2023-09-26 博格华纳汽车零部件（武汉）有限公司 New energy electric automobile howling noise evaluation method

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