Automobile acceleration noise evaluation method, equipment and storage medium

Abstract

The present invention discloses a method, device, and storage medium for evaluating automobile acceleration noise, relating to the field of automobile performance analysis. The method comprises the following steps: obtaining a set of noise signals within an engine speed range, segmenting the noise signal set to form a plurality of noise signal data segments; determining the MFCC features of each noise signal data segment, and determining an MFCC evaluation result based on each MFCC feature and its corresponding engine speed; determining a timbre mutation evaluation result based on the similarity of the MFCC features; and determining an automobile acceleration noise evaluation result based on the MFCC evaluation results and the timbre mutation evaluation results. The present invention determines the MFCC evaluation result of automobile acceleration noise based on each MFCC feature and its corresponding engine speed; and determines the timbre mutation evaluation result based on the similarity of the MFCC features of adjacent segments, thereby jointly determining the automobile acceleration noise evaluation result. The evaluation result thus obtained is relatively accurate, comprehensive, and detailed.

Description

Technical Field

[0001] The present invention relates to the field of automobile performance analysis, and in particular to an automobile acceleration noise evaluation method, equipment and storage medium. Background Art

[0002] Objective evaluation methods for vehicle acceleration noise generally involve measuring interior noise and engine speed signals, then processing the data to generate a curve of interior sound pressure level versus engine speed. For example, Chinese invention patent publication number CN117109938A discloses a method and device for evaluating vehicle acceleration noise quality. The method comprises the following steps: generating an acceleration noise waterfall plot based on the total interior noise during vehicle acceleration; extracting noise energy from the acceleration noise waterfall plot according to the order of the noise, based on a preset order bandwidth, to obtain the powertrain acceleration noise energy; determining the background noise energy during vehicle acceleration based on the total noise energy and the powertrain acceleration noise energy; and evaluating the vehicle's acceleration noise quality based on the relationship between the powertrain acceleration noise energy and the background noise energy and corresponding acceleration noise evaluation indicators. This method achieves a comprehensive evaluation of a vehicle's acceleration noise quality from both the powertrain acceleration noise and the background noise, thereby helping accurately position the vehicle and providing guidance for vehicle noise quality development. Summary of the Invention

[0003] In response to the defects in the existing technology, the technical problem solved by the present invention is: how to evaluate automobile acceleration noise based on MFCC (Mel Frequency Cepstrum Coefficient) so that the evaluation results match the auditory characteristics of the human ear, that is, conform to the user's subjective feelings, thereby improving the evaluation accuracy.

[0004] To achieve the above objectives, in a first aspect, an embodiment of the present application provides a method for evaluating automobile acceleration noise, the method comprising the following steps:

[0005] Acquire a noise signal set within an engine speed range, and segment the noise signal set to form a plurality of segments of noise signal data;

[0006] Determine the MFCC features of each noise signal data segment, and determine the MFCC evaluation result based on each MFCC feature and its corresponding engine speed;

[0007] The timbre mutation evaluation result is determined based on the similarity of MFCC features of adjacent segments;

[0008] The vehicle acceleration noise evaluation results are determined based on the MFCC evaluation results and the timbre mutation evaluation results.

[0009] In conjunction with the first aspect, in one embodiment, the process of obtaining the noise signal set within the engine speed range includes:

[0010] respectively obtaining an engine lower speed limit closest to a lower limit of the engine speed range and an engine upper speed limit closest to an upper limit of the engine speed range;

[0011] forming an engine speed set according to an upper engine speed limit, a lower engine speed limit, and other engine speeds between the upper engine speed limit and the lower engine speed limit;

[0012] The noise signals corresponding to all engine speeds in the engine speed set are taken as the noise signal set.

[0013] In combination with the first aspect, in one embodiment, the calculation formula for the segmentation time length T for segmenting the noise signal set is:

[0014] T=N*Δt, where N represents the number of sampling points of the noise signal in each noise signal set after segmentation, Δt represents the noise sampling interval, and Δt=1 / fs, where fs is the noise sampling frequency.

[0015] In conjunction with the first aspect, in one embodiment, the process of segmenting the noise signal set to form a plurality of segments of noise signal data includes:

[0016] Determine the total duration according to the minimum sampling time and the maximum sampling time in the noise signal set, and determine the time range of each segment according to the total duration and the segment duration;

[0017] The noise signal at the sampling moment belonging to each time range is taken as the noise signal data of that section.

[0018] In conjunction with the first aspect, in one embodiment, the process of determining the MFCC features of each segment of noise signal data and determining the MFCC evaluation result according to each MFCC feature and its corresponding engine speed includes:

[0019] Determine the MFCC features of each segment of noise signal data and calculate the MFCC amplitude of each MFCC feature vector;

[0020] Based on each MFCC amplitude and its corresponding engine speed, an evaluation curve of MFCC amplitude changing with engine speed is formed;

[0021] The MFCC evaluation result is determined according to the evaluation curve.

[0022] In conjunction with the first aspect, in one embodiment, the process of forming an evaluation curve of MFCC amplitude versus engine speed based on each MFCC amplitude and its corresponding engine speed includes:

[0023] The engine speed and MFCC amplitude are used as the horizontal and vertical coordinates respectively;

[0024] Determine the coordinate point based on each MFCC amplitude and its corresponding engine speed;

[0025] Connect all coordinate points to obtain the evaluation curve of MFCC amplitude changing with engine speed;

[0026] The process of determining the MFCC evaluation result based on the evaluation curve includes: generating a target line corresponding to the vehicle acceleration noise target requirement, judging whether the evaluation curve intersects with the target line, and if so, determining that the MFCC evaluation result is failed; otherwise, determining that the MFCC evaluation result is passed.

[0027] In combination with the first aspect, in one embodiment, the process of determining the timbre mutation evaluation result based on the similarity of adjacent MFCC features includes: calculating the cosine similarity of the MFCC feature vectors of adjacent segmented noise signal data; judging whether the number of times the cosine similarity is less than a reference threshold exceeds a specified value, and if so, determining that the timbre mutation evaluation result fails, otherwise determining that the timbre mutation evaluation result passes.

[0028] In combination with the first aspect, in one embodiment, the process of determining the automobile acceleration noise evaluation result based on the MFCC evaluation result and the timbre mutation evaluation result includes: judging whether both the MFCC evaluation result and the timbre mutation evaluation result are passed; if so, determining that the automobile acceleration noise evaluation result is passed; otherwise, determining that the automobile acceleration noise evaluation result is failed.

[0029] In a second aspect, an embodiment of the present application provides a vehicle acceleration noise evaluation device, comprising a processor, a memory, and a vehicle acceleration noise evaluation program stored in the memory and executable by the processor, wherein when the vehicle acceleration noise evaluation program is executed by the processor, the method provided in the first aspect is implemented.

[0030] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, characterized in that a vehicle acceleration noise evaluation program is stored on the computer-readable storage medium, wherein when the vehicle acceleration noise evaluation program is executed, the method provided in the first aspect is implemented.

[0031] Compared with the prior art, the advantages of the present invention are:

[0032] The present invention uses MFCC features to evaluate vehicle acceleration noise, fully considering the auditory characteristics of the human ear. At the same time, the present invention also determines the MFCC evaluation result of vehicle acceleration noise based on each MFCC feature and its corresponding engine speed; and determines the timbre mutation evaluation result based on the similarity of adjacent segmented MFCC features, thereby jointly determining the vehicle acceleration noise evaluation result. The evaluation result thus obtained is relatively accurate, comprehensive and detailed, and can meet people's requirements for high-quality acceleration noise. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0034] Figure 1 Schematic diagram of an evaluation curve of MFCC amplitude versus engine speed in an embodiment of the present invention;

[0035] Figure 2 Schematic diagram of comparison between the target line and the evaluation curve in an embodiment of the present invention;

[0036] Figure 3 Schematic diagram of comparison between the segmented target line and the evaluation curve in an embodiment of the present invention;

[0037] Figure 4 Schematic diagram of linearity in which the difference between the upper limit and the lower limit of the evaluation curve does not change with the engine speed in an embodiment of the present invention;

[0038] Figure 5 Schematic diagram of the linearity of the difference between the upper limit and the lower limit of the evaluation curve as the engine speed changes in an embodiment of the present invention;

[0039] Figure 6 Schematic diagram of the process of the vehicle acceleration noise evaluation method according to an embodiment of the present invention;

[0040] Figure 7 This is a schematic diagram of the hardware structure of the automobile acceleration noise evaluation device involved in the embodiment of the present application. DETAILED DESCRIPTION

[0041] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0042] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0043] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0044] First, the research and development process of the present invention is introduced.

[0045] Through subjective experience and market research, the inventors discovered that the acceleration noise evaluation results in the existing technology may be different from the user's perception. For example, the acceleration noise evaluation result of car A meets the requirements, but when the user uses car A, he or she may perceive uncomfortable (such as "harsh") noise.

[0046] The inventors further analyzed and concluded that the reason for the above situation is that the acceleration noise evaluation method in the prior art does not fully consider the auditory characteristics of the human ear.

[0047] To this end, the inventors conducted research and development to develop accelerated noise evaluation based on MFCCs that can adapt to the human hearing characteristics. Specifically, Mel is the unit of subjective pitch, while Hz (Hertz) is the unit of objective pitch. Mel frequency, based on the human hearing characteristics, has a nonlinear relationship with Hz. Mel-frequency cepstral coefficients (MFCCs) are cepstral features calculated by leveraging this relationship.

[0048] On this basis, in the first aspect, an embodiment of the present application provides a method for evaluating automobile acceleration noise, the steps of which include: obtaining a noise signal set under the engine speed range that needs to be evaluated, segmenting the noise signal set to form several segments of noise signal data (data blocks); determining the MFCC features of each segment of noise signal data, and determining the MFCC evaluation result based on each MFCC feature and its corresponding engine speed; determining the timbre mutation evaluation result based on the similarity of adjacent segmented MFCC features; and determining the automobile acceleration noise evaluation result based on the MFCC evaluation result and the timbre mutation evaluation result.

[0049] It can be seen that the present invention uses MFCC features to evaluate automobile acceleration noise, fully considering the auditory characteristics of the human ear; at the same time, the present invention also determines the MFCC evaluation result of automobile acceleration noise based on each MFCC feature and its corresponding engine speed; and determines the timbre mutation evaluation result based on the similarity of adjacent segmented MFCC features, to jointly determine the automobile acceleration noise evaluation result. The evaluation result obtained in this way is relatively accurate, comprehensive and detailed, and can meet people's requirements for high quality of acceleration noise.

[0050] In one embodiment, the engine speed range to be evaluated is explained as: evaluating the engine speed range corresponding to the vehicle speed accelerating from 0 to a specified speed, for example, 1000-5000 rpm, where rpm stands for revolutions per minute.

[0051] The process of obtaining a set of noise signals within the engine speed range that needs to be evaluated includes: respectively obtaining (sampling) the engine lower limit speed that is closest to the lower limit value of the engine speed range, and the engine upper limit speed that is closest to the upper limit value of the engine speed range; forming an engine speed set based on the engine upper limit speed, the engine lower limit speed, and other engine speeds between the engine upper limit speed and the engine lower limit speed (in array mode during execution, that is, an engine speed sequence).

[0052] The noise signal closest to the sampling moment of the engine speed (the noise signal is obtained by sampling, and the sampling method and sampling frequency can be the same as or different from the engine speed, and there is no restriction here) is defined as the noise signal corresponding to the engine speed; the noise signals corresponding to all engine speeds in the engine speed set are regarded as the noise signal set (in array form during execution, that is, noise signal sequence).

[0053] Furthermore, the reason why the above-mentioned values ​​are closest but not identical is that the engine speed range is a theoretical value, while the engine speed is obtained by actual sampling. It is possible that the upper or lower limit values ​​cannot be sampled. Therefore:

[0054] The above-mentioned process for determining the upper and lower engine speed limits includes:

[0055] When the engine speed sampled is the same as the upper limit or lower limit, the engine speed is used as the upper limit or lower limit speed;

[0056] When no engine speed that is the same as the upper limit or lower limit is sampled, the engine speed with the smallest absolute difference from the upper limit or lower limit is selected as the upper limit or lower limit speed;

[0057] When no engine speed that is the same as the upper limit or lower limit is sampled, and there are two or more engine speeds whose absolute difference from the upper limit or lower limit is the smallest and whose difference is the same: for the lower limit, the smallest engine speed is selected as the lower limit speed; for the upper limit, the largest engine speed is selected as the upper limit speed.

[0058] The reason for performing the above operation is that the noise signal and the engine speed signal originate from different sources: the interior noise signal originates from a microphone, while the engine speed signal originates from a speed sensor or the vehicle's CAN signal. Therefore, the sampling frequencies of these two signals are generally different. The sampling frequency of the interior noise, denoted as fs, is typically 40,000 Hz or 20,000 Hz, while the engine speed signal has a lower sampling frequency, typically a few hundred hertz, such as 256 Hz. This means that the two signals are not synchronized. Therefore, it is necessary to segment the noise signal set within the engine speed range and process it in sections.

[0059] On this basis, the calculation formula for the segmentation time length T of the noise signal set in the above method is: T = 1 / Δf = N / fs = N*Δt, where N represents the number of sampling points of the noise signal in each noise signal set after segmentation, N is an integer, Δt represents the noise sampling time interval, Δf represents the frequency resolution of spectrum analysis, fs is the noise sampling frequency, and Δt = 1 / fs.

[0060] Furthermore, the process of forming several segments of noise signal data after segmenting the noise signal set in the above method includes: determining the total duration based on the minimum sampling time and the maximum sampling time in the noise signal set, and determining the time range of each segment based on the total duration and the above segment duration; for example, if the acquisition segment duration is T and the minimum sampling time is t(1), then the time range of the first segment of noise signal is [t(1), t(1)+T); the noise signal whose acquisition time belongs to each time range is used as the noise signal data of that segment.

[0061] It should be noted that the sampling time in the previous paragraph can be the sampling time of the engine speed corresponding to the noise signal, or the sampling time of the noise signal itself. Regardless of which one, this solution can be implemented and is not limited here.

[0062] In one embodiment, the process of determining the MFCC features of each segment of noise signal data and determining the MFCC evaluation result based on each MFCC feature and its corresponding engine speed in the above method includes: determining the MFCC features of each segment of noise signal data (calculation of MFCC features is a known technique), and calculating the MFCC amplitude amp of each MFCC feature vector, using the following calculation formula: Where M represents the number of elements in the MFCC feature vector, and Ci represents the elements in the MFCC feature vector.

[0063] Based on each MFCC amplitude and its corresponding engine speed, an evaluation curve of MFCC amplitude changing with engine speed is formed; and the MFCC evaluation result is determined based on the evaluation curve.

[0064] It should be noted that the above-mentioned MFCC features or MFCC amplitudes and their corresponding engine speeds are the engine speeds corresponding to the noise signals specified in the noise signal data to which the MFCC features or MFCC amplitudes belong. The specified noise signal is the minimum or maximum noise signal at the time of acquisition.

[0065] Specifically, the process of forming an evaluation curve of MFCC amplitude versus engine speed based on each MFCC amplitude and its corresponding engine speed includes: Figure 1 As shown in the figure, a plane coordinate system is established, with engine speed and MFCC amplitude as the horizontal and vertical axes, respectively. Coordinate points are determined based on each MFCC amplitude and its corresponding engine speed. All coordinate points are connected to form an evaluation curve showing the variation of MFCC amplitude with engine speed. Generally speaking, higher speeds result in larger MFCC amplitudes. Therefore, the evaluation curve rises from the lower left corner to the upper right corner.

[0066] Furthermore, the above process of determining the MFCC evaluation result based on the evaluation curve includes: Figure 2 As shown, a target line corresponding to the vehicle acceleration noise target requirement is generated, and it is determined whether the evaluation curve intersects with the target line. If so, the MFCC evaluation result is determined to be failed, otherwise the MFCC evaluation result is determined to be passed.

[0067] It should be noted that, see Figure 3 As shown, when judging by the above target line, a segmented target line can be set, that is, different requirements are placed on the slope of the target line in different speed sections.

[0068] At the same time, the above process of determining the MFCC evaluation result based on the evaluation curve also includes: setting the linearity requirement of the evaluation curve, and when the evaluation curve does not intersect the target line and the linearity meets the requirement, determining that the MFCC evaluation result is passed.

[0069] The reason for this implementation is: setting linearity requirements can ensure that the evaluation curve has a smaller tolerance at each speed, making the evaluation curve more linear and making the in-vehicle noise feel smoother and more silky.

[0070] The specific method to set the linearity requirement is: Figure 4As shown, according to the principle of least squares method, a slope line is fitted to the level vs. rpm curve, which is called the middle line. In the present invention, linearity tolerance is used to evaluate linearity, and the calculation formula of linearity tolerance is: Where a is the maximum distance between the evaluation curve and the midline along the ordinate, and current_val is the ordinate of a point on the midline. The calculation formula shows that smaller linearity tolerances indicate better linearity, while larger linearity tolerances indicate worse linearity.

[0071] Furthermore, this embodiment provides two ways to set linearity requirements:

[0072] (1) When current_val is a constant (the vertical coordinate of a point on the middle line), after the linearity tolerance linearity is determined, a is a fixed value. Draw a slant line on both sides of the middle line with a vertical distance a from the middle line. Both slant lines are parallel to the middle line, thus determining the upper and lower limits of the evaluation curve, as shown in the following example: Figure 4 When the vehicle's evaluation curve is between these two oblique lines, the linearity meets the requirement; otherwise, it does not meet the requirement.

[0073] (2) When current_val is not a constant and is the vertical coordinate of the current point on the middle line, after the linearity tolerance linearity is determined, a changes with the change of the current point on the middle line. Draw a slant line on both sides of the middle line, and the vertical distance between the point on the slant line and the corresponding point on the middle line is a. The two slant lines are not parallel to the middle line, but are trumpet-shaped; thus, the upper and lower limits of the evaluation curve are determined, such as Figure 5 When the evaluation curve lies between these two lines, the linearity meets the requirements; otherwise, it fails. Setting the linearity in the second method is practically reasonable. Higher engine speeds increase the number of uncontrollable factors (such as the greater inertia of internal engine components and the closer the excitation frequency approaches the natural frequency of each component), resulting in greater interior noise. Therefore, the noise tolerance should be set more loosely.

[0074] In one embodiment, the process of determining the timbre mutation evaluation result based on the similarity of adjacent MFCC features in the above method includes: calculating the cosine similarity of the MFCC feature vectors of adjacent segmented noise signal data; judging whether the number of times the cosine similarity is less than the reference threshold exceeds a specified value, and if so, determining that the timbre mutation evaluation result fails, otherwise determining that the timbre mutation evaluation result passes.

[0075] It should be noted that the above-mentioned reference thresholds and specified values ​​are set according to actual needs; the principle of the above-mentioned embodiment is that the range of cosine similarity is [-1,1], and the larger the value, the closer the two vectors are; the smaller the value, the less similar the two vectors are. Therefore, if the cosine similarity of adjacent segments is large, it is considered that the timbre has not changed much and the timbre has not suddenly changed; conversely, if the cosine similarity of adjacent segments is small, it is considered that the timbre has suddenly changed, which will give people an uncomfortable feeling and should be avoided. How to determine whether the cosine similarity is small, obviously, requires setting a threshold. Since the cosine similarity value range is [-1,1], naturally, this threshold can be set to 0. That is, if the cosine similarity ≤ 0, it is considered that the timbre has suddenly changed.

[0076] In one embodiment, the process of determining the vehicle acceleration noise evaluation result based on the MFCC evaluation result and the timbre mutation evaluation result in the above method includes: judging whether both the MFCC evaluation result and the timbre mutation evaluation result are passed; if so, determining that the vehicle acceleration noise evaluation result is passed; otherwise, determining that the vehicle acceleration noise evaluation result is failed.

[0077] In summary, the present invention has a simple principle and a simple algorithm. The MFCC features adopted take into account the auditory characteristics of the human ear and are consistent with people's subjective feelings. The three indicators of target line, linearity, and timbre mutation are used to evaluate acceleration noise. The evaluation is more accurate, comprehensive, and detailed, meeting people's requirements for high quality of acceleration noise.

[0078] See below Figure 6 As shown, the method of the present invention is described through a specific embodiment.

[0079] S1: Obtain a set of noise signals within the engine speed range to be evaluated.

[0080] The specific process of S1 includes:

[0081] S101: Set the segment duration T of the noise signal. The calculation formula of T is as described above.

[0082] S102: Set the engine speed range to be evaluated, specifically 1000-5000 rpm, where rpm represents revolutions per minute. Set the lower limit of the speed range to low_rpm and the upper limit to up_rpm. Naturally, the set speed range must be within the actual measured speed range.

[0083] S103: Search the speed signal: Find the speed data closest to low_rpm, denoted as rpm_array(1). rpm_array is the speed array, rpm_array(1) is its first element, and the time at which it occurs is t_temp. Search the noise signal for the sampling time closest to t_temp, denoted as t(1), where t is the time array and t(1) is its first element.

[0084] S2: Segment the noise signal set to form several noise signal data blocks.

[0085] The specific process of S2 includes:

[0086] Taking t(1) as the starting time of noise signal analysis and T as the segment duration, the first analysis data block is determined, and its time range is [t(1), t(1)+T); the speed corresponding to this data block is rpm_array(1).

[0087] Therefore, the time corresponding to the first analysis data block is t(1), and the corresponding speed is rpm_array(1). The starting time of the second analysis data block is t(2), t(2) = t(1) + T; then, the speed corresponding to the sampling time closest to t(2) is searched in the speed signal, and is recorded as rpm_array(2). Then, the time range corresponding to the second analysis data block is [t(2), t(2) + T), and its corresponding time is t(2), and its corresponding speed is rpm_array(2). ... The starting time of the jth analysis data block is t(j), t(j) = t(j-1) + T; then, the speed corresponding to the sampling time closest to t(j) is searched in the speed signal, and is recorded as rpm_array(j). Then, the time range corresponding to the jth analysis data block is [t(j), t(j) + T), and its corresponding time is t(j), and its corresponding speed is rpm_array(j). If rpm_array(j)>=up_rpm, S2 ends, and the arrays rpm_array and t are the speed sequence and time series; otherwise, continue this process.

[0088] As needed, the overlap rate parameter can be set, that is, there is an overlapping area between adjacent analysis data blocks so that the noise signal can be fully utilized. The specific analysis process is similar to S2 and will not be repeated here.

[0089] S3: Determine the MFCC features of each noise signal data block and calculate the MFCC amplitude of each MFCC feature vector. The calculation method is as described above.

[0090] S4: Based on each MFCC amplitude and its corresponding engine speed, an evaluation curve is formed to show how the MFCC amplitude changes with the engine speed. The MFCC evaluation result is determined based on the evaluation curve. The determination method is: generating a target line corresponding to the vehicle acceleration noise target requirement, and determining whether the evaluation curve intersects with the target line. If so, the MFCC evaluation result is determined to be failed; otherwise, the MFCC evaluation result is determined to be passed.

[0091] S5: Determine the timbre mutation evaluation result based on the cosine similarity of the MFCC feature vectors of the noise signal data blocks of adjacent segments, specifically: determine whether the number of times the cosine similarity is less than the reference threshold exceeds a specified value; if so, determine that the timbre mutation evaluation result fails; otherwise, determine that the timbre mutation evaluation result passes.

[0092] S6: Determine the vehicle acceleration noise evaluation result based on the MFCC evaluation result and the timbre mutation evaluation result, specifically: determine whether the MFCC evaluation result and the timbre mutation evaluation result are both passed; if so, determine that the vehicle acceleration noise evaluation result is passed; otherwise, determine that the vehicle acceleration noise evaluation result is failed.

[0093] In a second aspect, an embodiment of the present application provides a vehicle acceleration noise evaluation device, which may be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.

[0094] Reference Figure 7 , Figure 7 FIG2 is a schematic diagram of the hardware structure of the automobile device involved in the embodiment of the present application. In the embodiment of the present application, the automobile acceleration noise evaluation device may include a processor, a memory, a communication interface, and a communication bus.

[0095] The communication bus may be of any type and is used to interconnect the processor, memory, and communication interface.

[0096] Communication interfaces include input / output (I / O), physical, and logical interfaces, which interconnect components within the vehicle acceleration noise evaluation device and other devices (such as other computing devices or user devices). Physical interfaces can include Ethernet, fiber optic, and ATM interfaces; user devices can include displays and keyboards.

[0097] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0098] The processor may be a general-purpose processor that can invoke a vehicle acceleration noise evaluation program stored in a memory and execute the vehicle acceleration noise evaluation method provided in the embodiments of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the vehicle acceleration noise evaluation program is invoked can be referenced from the various embodiments of the vehicle acceleration noise evaluation method of the present application and will not be further described here.

[0099] Those skilled in the art will understand that Figure 7 The hardware structure shown in the figure does not constitute a limitation to the present application and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0100] In a third aspect, an embodiment of the present application also provides a computer-readable storage medium.

[0101] The computer-readable storage medium of the present application stores a vehicle acceleration noise evaluation program, wherein when the vehicle acceleration noise evaluation program is executed by a processor, the steps of the vehicle acceleration noise evaluation method described above are implemented.

[0102] Among them, the method implemented when the automobile acceleration noise evaluation program is executed can refer to the various embodiments of the automobile acceleration noise evaluation method of the present application, and will not be repeated here.

[0103] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0104] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for enabling a terminal device to execute the methods described in each embodiment of the present application.

[0105] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit the "first", "second" and "third" to different types.

[0106] In the description of the embodiments of this application, the words "exemplary," "for example," or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary," "for example," or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "for example," or "for example" is intended to present the relevant concepts in a concrete manner.

[0107] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0108] In some processes described in the embodiments of the present application, multiple operations or steps are included that appear in a specific order. However, it should be understood that these operations or steps may not be performed in the order in which they appear in the embodiments of the present application or may be performed in parallel. The sequence numbers of the operations are only used to distinguish between different operations, and the sequence numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations or steps may be performed in sequence or in parallel, and these operations or steps may be combined.

[0109] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for enabling a terminal device to execute the methods described in each embodiment of the present application.

[0110] The above are only specific implementations of the embodiments of the present invention, but the scope of protection of the embodiments of the present invention is not limited to them. Any person skilled in the art can easily conceive of various equivalent modifications or replacements within the technical scope disclosed in the embodiments of the present invention, and such modifications or replacements should be included in the scope of protection of the embodiments of the present invention. Therefore, the scope of protection of the embodiments of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for evaluating automobile acceleration noise, characterized in that: The method comprises the following steps: Acquire a noise signal set within an engine speed range, and segment the noise signal set to form a plurality of segments of noise signal data; Determine the MFCC features of each noise signal data segment, and determine the MFCC evaluation result based on each MFCC feature and its corresponding engine speed; The timbre mutation evaluation result is determined based on the similarity of MFCC features of adjacent segments; The vehicle acceleration noise evaluation results are determined based on the MFCC evaluation results and the timbre mutation evaluation results.

2. The vehicle acceleration noise evaluation method according to claim 1, wherein: The process of obtaining the noise signal set within the engine speed range includes: respectively obtaining an engine lower speed limit closest to a lower limit of the engine speed range and an engine upper speed limit closest to an upper limit of the engine speed range; forming an engine speed set according to an upper engine speed limit, a lower engine speed limit, and other engine speeds between the upper engine speed limit and the lower engine speed limit; The noise signals corresponding to all engine speeds in the engine speed set are taken as the noise signal set.

3. The vehicle acceleration noise evaluation method according to claim 1, wherein: The calculation formula for the segmentation time length T of the noise signal set is: T=N*Δt, where N represents the number of sampling points of the noise signal in each noise signal set after segmentation, Δt represents the noise sampling interval, and Δt=1 / fs, where fs is the noise sampling frequency.

4. The vehicle acceleration noise evaluation method according to claim 3, wherein: The process of segmenting the noise signal set to form a plurality of noise signal data segments includes: Determine the total duration according to the minimum sampling time and the maximum sampling time in the noise signal set, and determine the time range of each segment according to the total duration and the segment duration; The noise signal at the sampling moment belonging to each time range is taken as the noise signal data of that section.

5. The vehicle acceleration noise evaluation method according to claim 1, wherein: The process of determining the MFCC features of each segment of noise signal data and determining the MFCC evaluation result according to each MFCC feature and its corresponding engine speed includes: Determine the MFCC features of each segment of noise signal data and calculate the MFCC amplitude of each MFCC feature vector; Based on each MFCC amplitude and its corresponding engine speed, an evaluation curve of MFCC amplitude changing with engine speed is formed; The MFCC evaluation result is determined according to the evaluation curve.

6. The vehicle acceleration noise evaluation method according to claim 5, wherein: The process of forming an evaluation curve of MFCC amplitude versus engine speed based on each MFCC amplitude and its corresponding engine speed includes: The engine speed and MFCC amplitude are used as the horizontal and vertical coordinates respectively; Determine the coordinate point based on each MFCC amplitude and its corresponding engine speed; Connect all coordinate points to obtain the evaluation curve of MFCC amplitude changing with engine speed; The process of determining the MFCC evaluation result based on the evaluation curve includes: generating a target line corresponding to the vehicle acceleration noise target requirement, judging whether the evaluation curve intersects with the target line, and if so, determining that the MFCC evaluation result is failed; otherwise, determining that the MFCC evaluation result is passed.

7. The vehicle acceleration noise evaluation method according to claim 1, wherein: The process of determining the timbre mutation evaluation result based on the similarity of adjacent MFCC features includes: calculating the cosine similarity of the MFCC feature vectors of adjacent segmented noise signal data; judging whether the number of times the cosine similarity is less than a reference threshold exceeds a specified value, and if so, determining that the timbre mutation evaluation result fails, otherwise determining that the timbre mutation evaluation result passes.

8. The vehicle acceleration noise evaluation method according to any one of claims 1 to 7, characterized in that: The process of determining the vehicle acceleration noise evaluation result based on the MFCC evaluation result and the timbre mutation evaluation result includes: judging whether both the MFCC evaluation result and the timbre mutation evaluation result are passed; if so, determining that the vehicle acceleration noise evaluation result is passed; otherwise, determining that the vehicle acceleration noise evaluation result is failed.

9. A vehicle acceleration noise evaluation device, characterized in that: The vehicle acceleration noise evaluation device includes a processor, a memory, and a vehicle acceleration noise evaluation program stored in the memory and executable by the processor. When the vehicle acceleration noise evaluation program is executed by the processor, the steps of the vehicle acceleration noise evaluation method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a vehicle acceleration noise evaluation program, wherein when the vehicle acceleration noise evaluation program is executed, the steps of the vehicle acceleration noise evaluation method according to any one of claims 1 to 8 are implemented.

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