Headphone distortion measurement method based on psychoacoustics, storage medium, and headphone

Abstract

The present invention relates to a headphone distortion measurement method, storage medium, and headphone based on psychoacoustics, wherein the method comprises: using a stepped sinusoidal frequency sweep signal as an excitation signal, and exciting the headphone through the excitation signal to obtain a response signal of the headphone; subjecting each frequency point of the excitation signal and the response signal to a psychoacoustic model based on the human masking effect, and obtaining a noise loudness NL[k] for representing distortion and noise at each frequency point; performing a harmonic analysis on each frequency point of the response signal, and obtaining a percentage measurement result Rub&Buzz(f) of the harmonic analysis at each frequency point; multiplying the percentage measurement result Rub&Buzz(f) of each frequency point by the noise loudness NL[k], and obtaining a test result of the headphone sound quality based on the multiplication result. The present invention highlights the distortion curve of the abnormal sound generated by the faulty headphone from the perspective of human hearing, and realizes the distortion detection of the abnormal sound generated by the headphone, and the method is simple and clear, and easy to implement.

Description

Technical Field

[0001] The invention relates to the field of earphone sound quality testing, and in particular to an earphone distortion measurement method based on psychoacoustics, a storage medium, and an earphone. Background Art

[0002] During the production process of earphones, faulty earphones may occur. Faulty earphones will produce friction sound and buzzing sound (Rub&Buzz), which will bring annoying sharp sounds. This type of abnormal sound will cause very obvious distortion. Therefore, it is necessary to detect Rub&Buzz to detect faults and help recycle faulty earphones.

[0003] Manufacturing defects in headphone production can often be identified by an increase in rub and buzz distortion. This type of distortion is very noticeable because it introduces a sharp sound to the reproduction, and is annoying because it is often separate or disembodied from the underlying signal. The annoyance of rub and buzz distortion is closely related to the human perception of sound and psychoacoustics. In order to properly implement automated production line responses for headphone distortion due to rub and buzz defects, the auditory process must be modeled or simulated using a sufficiently accurate perceptual model. The present invention describes the results of a rub and buzz detection system using a simplified perceptual model based on the human masking effect that produces good results.

[0004] Therefore, a sufficiently accurate perceptual model to model the auditory process and detect Rub&Buzz distortion has a positive guiding role in companies troubleshooting headphone failures and adjusting production lines. Summary of the invention

[0005] To achieve the above object, the present invention proposes a headphone distortion measurement method based on psychoacoustics, a storage medium, and a headphone.

[0006] Among them, as a first aspect of the present invention, a headphone distortion measurement method based on psychoacoustics is proposed, comprising:

[0007] Step S1. Using a stepped sine sweep signal as an excitation signal, the earphone is excited by the excitation signal to obtain a response signal of the earphone;

[0008] Step S2. Each frequency point of the excitation signal and the response signal is subjected to a psychoacoustic model based on the human masking effect to obtain the noise loudness NL[k] of each frequency point for representing distortion and noise;

[0009] Step S3. Perform harmonic analysis on each frequency point of the response signal to obtain a percentage measurement result Rub&Buzz(f) of the harmonic analysis of each frequency point;

[0010] Step S4. Multiply the percentage measurement result Rub&Buzz(f) of each frequency point by the noise loudness NL[k], and obtain the test result of the headphone sound quality based on the multiplication result.

[0011] As a second aspect of the present invention, the proposed storage medium should be understood as a computer-readable storage medium, which stores one or more programs, and when the one or more programs are executed by a processor, the above method is implemented.

[0012] As a third aspect of the present invention, a headset is provided that includes a processor and an audio output device electrically connected to the processor, and also includes a memory arranged to store computer executable instructions, which, when executed, enable the processor to perform the above method.

[0013] The present invention proposes a distortion detection algorithm for abnormal sound of headphones based on psychoacoustics in the automatic detection system of headphones. From the perspective of human hearing, the distortion curve of the abnormal sound produced by the faulty headphones is highlighted to realize the distortion detection of the abnormal sound produced by the headphones. The method is simple and clear and easy to implement. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 The main flow chart of the headphone distortion measurement method of the present invention is shown.

[0015] Figure 2 A schematic flow chart of the psychoacoustic algorithm model of the present invention is shown.

[0016] Figure 3 Shows the transmission characteristics of sound through a bandpass filter from the outer ear to the inner ear.

[0017] Figure 4 The transmission characteristics of noise inside the human body are shown.

[0018] Figure 5 A schematic diagram showing the conversion of the FFT spectrum to the Bark scale.

[0019] Figure 6 The transmission characteristics of hair cells acting as nonlinear bandpass filters are shown.

[0020] Figure 7 The expansion function corresponding to a 1 khz sine wave is shown.

[0021] Figure 8 The calculation result of noise loudness obtained by using the loudness values ​​of excitation and response is shown.

[0022] Fig. 9 The results of high-order harmonic separation after harmonic analysis of each frequency point of the response signal are shown.

[0023] Fig.10 A schematic of the distortion curve is shown.

[0024] Fig.11 The specific algorithm flow operation steps and sequence of the headphone distortion measurement method of the present invention are shown.

[0025] Fig.12 A schematic diagram of the architecture of the headset on the program system is shown.

[0026] Fig.13 A schematic diagram of the structure of a computer-readable storage medium is shown. DETAILED DESCRIPTION

[0027] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0028] The earphones referred to in the present invention should be understood as conventional earphones, including but not limited to bone conduction earphones, air conduction earphones, etc.

[0029] Figure 1 FIG. 2 shows a schematic diagram of the main flow of the headphone distortion measurement method of the present invention. Figure 1 As shown, the headphone distortion measurement method of this embodiment may include the following steps S1-S4, wherein:

[0030] Step S1. Using a stepped sine sweep signal as an excitation signal (also called an excitation signal), the earphone is excited by the excitation signal to obtain a response signal of the earphone (also called a response signal).

[0031] In this step, the headphone response at all frequencies can be measured by sweeping the high frequency to the low frequency using a frequency step sweep stimulus. The default sweep direction for the sine sweep signal is from high to low. The fundamental frequency response of the headphone does not change with the sweep direction or the response signal because it is a linear approximation. Since the speaker itself is nonlinear, care should be taken to get a reliable and repeatable linear response, such as the fundamental response. Nonlinear responses, such as distortion, are another matter and are more affected by the way the response signal is applied. The goal is to get the speaker to a stable state before measuring it to get repeatable results. When sweeping from low to high frequencies, the speaker will see a burst of energy at the first low frequency and can continue to ring throughout the measurement until it reaches a stable state. When sweeping from high to low frequencies, the energy of the first high frequency is very small compared to the low frequency. It reaches stability faster, so there is less possibility of the speaker ringing throughout the measurement.

[0032] After obtaining the excitation signal and the response signal, they are divided into frames according to the length of each frequency step (the frame length is the excitation data length of each frequency point), and then the data of each frame is processed (FFT) to obtain the excitation signal spectrum and the response signal spectrum.

[0033] Step S2: Each frequency point of the excitation signal and the response signal is subjected to a psychoacoustic model based on the human masking effect to obtain the noise loudness NL[k] of each frequency point used to represent distortion and noise.

[0034] In this step, before each frequency point of the excitation signal and the response signal passes through the psychoacoustic model, the spectrum of the excitation signal and the spectrum of the response signal are first calibrated. Because the psychoacoustic model is used, all input data must be in the hearing loss level (dBSPL). The spectrum level of the excitation signal is an internal reference, so when calculating, it should be proportionally adjusted to match the spectrum level of the response signal. The adjusted spectrum is represented by F[f], where f is the frequency.

[0035] The spectrum of the excitation signal and the spectrum of the response signal after level calibration are calculated through the psychoacoustic model.

[0036] (1) The human ear has low sensitivity at low and high frequencies and high sensitivity at medium frequencies. Therefore, the weight is low at low and high frequencies and high at medium frequencies. This is the basis for the present invention to simulate the hearing threshold curve of the human ear and the weights of different frequencies; (2) As the sound propagates in the ear, the internal noise generated by the human blood flow is simulated and is the largest at low frequencies; (3) The auditory filter is used to simulate different critical frequencies. The structure of the human ear will roughly resonate with 24 frequency points, and the signal also presents 24 critical frequency points in terms of frequency, from 1 to 24. This is the Bark domain, which is a psychoacoustic scale of sound.

[0037] For the psychoacoustic model, the above three points are used as theoretical basis for construction, and the frequency expansion is performed using the expansion function, and each frequency group is expanded separately. Specifically, the algorithm model is as follows Figure 2 As shown, steps S21-S24 are included, wherein:

[0038] Step S21: simulate the frequency response of the human ear, and filter each frequency point of the excitation signal and the response signal through the frequency response to achieve the corresponding energy Pp[f].

[0039] As an implementation scheme of step S21, the weight W[f] of each frequency point of the human ear can be obtained according to the physiological characteristics of the human ear, and each frequency point of the excitation signal and the response signal can be weighted according to the weight W[f], and the energy P of each frequency point after weighted processing can be obtained based on the calculation result. W[f]; simulate the internal noise of the human body and convert the energy P of each frequency point W [f] plus the energy E[f] of the internal noise at each frequency point as the energy Pp[f].

[0040] Specifically, according to the physiological characteristics of the human ear, that is, from the outer ear to the inner ear, the sound passes through the bandpass filter (the sensitivity of the human ear to frequency), and its transmission characteristics are as follows: Figure 3 As shown, according to Figure 3 Mathematical modeling is performed, and the corresponding formula of human hearing weight is simulated as follows:

[0041]

[0042] The above formula shows a weight corresponding to each frequency point, which is then weighted to F[f] using the following formula, as follows:

[0043]

[0044] Finally got F w [f], in fact, corresponds to the energy P w [f].

[0045] Simulates the noise inside the human body. As the sound propagates in the ear, the internal noise generated by the blood flow in the human body is simulated. It is the largest at low frequencies. Its transmission characteristics are as follows: Figure 4 As shown, the corresponding human body internal noise formula obtained by this modeling is as follows:

[0046]

[0047] The energy P of each frequency point w [f] plus the energy E[f] of the internal noise at each frequency point, we get the output Pp[f] of this step.

[0048] The above two steps (weighting and internal noise) jointly simulate the frequency response of the ear, and finally obtain the energy value corresponding to each frequency point after filtering by simulating the human ear and adding the internal noise corresponding to each frequency point.

[0049] Step S22: Convert the energy Pp[f] of each frequency point from the FFT spectrum to Pp[k] of the Bark scale.

[0050] This step aims to simulate the auditory filter of the human ear to simulate different critical frequencies. The structure of the human ear will roughly resonate with 24 frequency points, and the signal also presents 24 critical frequency points in terms of frequency, from 1 to 24. This is the Bark domain, which is a psychoacoustic scale of sound.

[0051] When implemented, the conversion of the FFT spectrum (constant bandwidth) to the Bark scale (auditory filter band) is shown below: Figure 5 As shown, the corresponding conversion formula is:

[0052]

[0053] Use the formula to convert f into bark, that is, convert f into bark on the horizontal axis, and for the vertical axis, it converts the energy Pp[f] into Pp[k].

[0054] Step S23: The energy Pp[k] of each frequency point under the Bark scale is frequency expanded using an expansion function to simulate the masking effect, so as to obtain the energy E[k] corresponding to each frequency point after processing.

[0055] This step aims to perform frequency expansion using an expansion function, which simulates a psychoacoustic filter to simulate the masking effect of the human ear:

[0056] like Figure 6 As shown, each hair cell acts as a nonlinear bandpass filter. It is characterized by a triangular shape. The shape is nearly constant along the scale of the bark. The lower slope is +27dB / bark and the upper slope varies with sound level, from -5 to -30dB. Because the filter characteristics overlap, a sine wave will excite a range of hair cells, with the louder the sound, the wider the range. When multiple components are present, the different corresponding excitations accumulate in a nonlinear manner. The frequency content is then smeared along the pitch scale. The net effect of the smearing is that the threshold of hearing is raised (masked) when the frequency is above or below a certain tone. Therefore, a weaker adjacent tone (masked) may not be heard if it is too close to the mask. Because the tone at about 1kHz is stronger, the tone at about 3kHz is inaudible.

[0057] The physiological effects of the human ear create an internal representation of the sound that constitutes the information given to the brain. Beyond this, our cognitive processes, where the brain interprets the data, intervene psychologically. For example, an added nonlinear component is more annoying than a linear distortion. On the other hand, a complex sound can mask the distortion. At this level, the brain will select the relevant information.

[0058] Figure 7 The figure shows an extended function corresponding to a 1 kHz sine wave. The left side of 1 kHz (corresponding to the bark unit in the figure) is the downward slope S. l , you can see that there are parallel straight lines for different pitches.

[0059] Uphill Slope u It is calculated using the following formula:

[0060]

[0061] Where L[k] / dB = 10·log 10 (P p [k]) Pp[k] is the ordinate value corresponding to the energy converted to the Bark scale, and Bark is the abscissa value of the Bark scale.

[0062] Spread spectrum for each frequency group k separately:

[0063]

[0064] Where γ is the compression mixing index, which is taken as 0.4.

[0065] Substituting L[k]=0 into Calculate and use the calculated value as Norm SP [k].

[0066] in,

[0067]

[0068] For A[j], it is the total energy of the spread function, and res is the resolution index of the mid-pitch scale of the Bark scale, which is set to 0.25.

[0069] The energy corresponding to each frequency point processed in step S23 is E[k].

[0070] Step S24: Use the energy E of each frequency point of the response signal after the expansion process ref [k], minus the energy E of the corresponding frequency point of the excitation signal after the expansion processing test [k], and the noise loudness NL[k] of the corresponding frequency point is calculated based on the obtained energy difference.

[0071] This step is designed to produce the specific loudness of the noise if no mask is present, or the ratio of the noise to the mask if the noise is very small compared to the mask.

[0072] In this step, the noise loudness NL[k] is calculated according to the following formula:

[0073]

[0074] Where const is the calibration factor, which is a constant.

[0075] When E ref When it is negligible, the loudness of NL is equal to E Thres Loudness, E Thres is a function that simulates the internal noise of the human body

[0076] The coefficient β determines the amount of masking and is calculated using the following formula:

[0077]

[0078] The final global value TotalNL is the overall noise loudness of NL[k]:

[0079]

[0080] For a frequency point, the calculated value is such a value. Because our input is a step-by-step frequency sweep signal, for different frequency points, the corresponding noise loudness value is obtained, such as Figure 8 shown.

[0081] Step S3. Perform harmonic analysis on each frequency point of the response signal to obtain a percentage measurement result Rub&Buzz(f) of the harmonic analysis of each frequency point;

[0082] Specifically, a step-by-step FFT transform is performed on the response signal of each step after weighting to obtain the spectrum, and a tracking filter is used to locate the harmonics of each frequency point, and the high-order harmonic components corresponding to the fundamental frequency are extracted. The percentage measurement result Rub&Buzz(f) of the harmonic analysis is calculated based on the high-order harmonic components.

[0083] The calculation formula is as follows:

[0084]

[0085] Fig. 9 The results of high-order harmonic separation after harmonic analysis of each frequency point of the response signal are shown.

[0086] Step S4. Multiply the percentage measurement result Rub&Buzz(f) of each frequency point by the noise loudness NL[k], and obtain the test result of the headphone sound quality based on the multiplication result.

[0087] Specifically, a distortion curve is produced based on the result of multiplying the percentage measurement result Rub&Buzz(f) of each frequency point by the noise loudness NL[k] to set a qualified threshold curve. If the distortion curve does not exceed the threshold curve, a qualified result of the headphone sound quality is output, otherwise it is considered an unqualified product.

[0088] As an example, the distortion curve is as follows Fig.10 As shown, it is obvious that the bad headphones have a strong harmonic structure due to the high audible levels of their abnormal sounds.

[0089] Of course, the product of the percentage measurement result Rub&Buzz(f) of each frequency point and the noise loudness NL[k] can also be obtained. If the product result of the frequency point does not exceed the threshold set for the frequency point, the headphone sound quality is output as qualified.

[0090] For ease of understanding, the present invention provides Fig.11 It should be understood that the specific algorithm flow operation steps shown should be interpreted as an example description.

[0091] The present invention also provides a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by a processor, the above-mentioned method is implemented. In other words, the above-mentioned method can be converted into program steps and devices that can be stored in a computer storage medium, and the processor calls and executes the program to implement it.

[0092] The present invention also provides a headset, comprising a processor, an audio output device electrically connected to the processor, and a memory arranged to store computer executable instructions, wherein when the executable instructions are executed, the processor executes the above method.

[0093] It should be noted that:

[0094] The algorithm and display provided herein are not inherently related to any particular computer, virtual device or other equipment. Various general-purpose devices can also be used together with the teachings based on this. According to the above description, it is obvious to construct the structure required for this type of device. In addition, the present invention is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to realize the content of the present invention described herein, and the description of the above specific language is for the purpose of disclosing the best mode of the present invention.

[0095] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0096] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting the intention that the claimed invention requires more features than those explicitly recited in each claim. More specifically, as reflected in the claims below, inventive aspects lie in less than all the features of the individual embodiments disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, with each claim itself serving as a separate embodiment of the present invention.

[0097] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition they may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0098] In addition, those skilled in the art will appreciate that, although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments. For example, in the claims below, any one of the claimed embodiments may be used in any combination.

[0099] The various component embodiments of the present invention can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for performing a part or all of the methods described herein. Such a program implementing the present invention can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0100] For example, Fig.12A schematic diagram of the architecture of the earphone according to the present invention on a program system is shown. The earphone conventionally includes a processor 101 and a memory 102 arranged to store computer executable instructions (program code). The memory 102 can be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read-only memory), an EPROM, a hard disk or a ROM. The memory 102 has a storage space 103 for storing a program code 104 for executing any method steps in the embodiment. For example, the storage space 103 for program code may include individual program codes 104 respectively used to implement various steps in the above method. These program codes can be read from or written to one or more computer program products. These computer program products include program code carriers such as a hard disk, a compact disk (CD), a memory card or a floppy disk. Such a computer program product is typically, for example Fig.13 The computer-readable storage medium may have Fig.12 The memory 102 in the earphone of the embodiment of the present invention is similar to the memory segment, storage space, etc. The program code can be compressed in a suitable form. Generally, the storage unit stores program code 111 for executing the steps of the method according to the present invention, that is, program code that can be read by a processor such as 101, and when these program codes are run by the earphone, the earphone is caused to execute the steps of the method described above.

[0101] It should be noted that the embodiments are illustrative rather than limiting of the invention, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be construed as a limitation of the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising a number of different elements and by means of a suitably programmed computer. In a unit claim enumerating a number of devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc., does not indicate any order. These words may be interpreted as names.

Claims

1. A method for measuring headphone distortion based on psychoacoustics, characterized in that: include: Step S1. Using a stepped sine sweep signal as an excitation signal, the earphone is excited by the excitation signal to obtain a response signal of the earphone; Step S2. Each frequency point of the excitation signal and the response signal is subjected to a psychoacoustic model based on the human masking effect to obtain the noise loudness NL[k] of each frequency point for representing distortion and noise; Step S3. Perform harmonic analysis on each frequency point of the response signal to obtain a percentage measurement result Rub&Buzz(f) of the harmonic analysis of each frequency point; Step S4. Multiply the percentage measurement result Rub&Buzz(f) of each frequency point by the noise loudness NL[k], and obtain the test result of the headphone sound quality based on the multiplication result; The psychoacoustic model in step S2 further comprises: Step S21. Simulate the frequency response of the human ear, and filter each frequency point of the excitation signal and the response signal through the frequency response to achieve the corresponding energy Pp[f]; Step S22. Convert the energy Pp[f] of each frequency point from the FFT spectrum to Pp[k] of the Bark scale; Step S23. The energy Pp[k] of each frequency point under the Bark scale is frequency expanded using an expansion function to simulate the masking effect, and the energy E[k] corresponding to each frequency point after processing is obtained; Step S24: Use the energy E after the expansion process at each frequency point of the response signal ref[k] , minus the energy E after the expansion processing of the corresponding frequency point of the excitation signal test[k] , the noise loudness NL[k] of the corresponding frequency point is calculated according to the obtained energy difference; The step S23 further comprises: according to Spread spectrum for each frequency point separately. Where γ is the compression mixing index, k represents the digital sequence number of the frequency group, L[k] / dB=10·log 10 (P p [k]), Pp[k] is the ordinate value corresponding to the energy converted to the Bark scale, and Bark is the abscissa value of the Bark scale. Substituting L[k]=0 into Calculate the value obtained as the Norm SP [k], A[j] is the total energy of the expansion function, res is the resolution index of the mid-pitch scale of the Bark scale; The step S24 further comprises: The noise loudness is calculated according to the formula calculate, Where const is the calibration factor, E Thres is a function that simulates the internal noise of the human body coefficient 2. The headphone distortion measurement method according to claim 1, characterized in that: The step scanning direction of the sinusoidal frequency sweep signal in step S1 is configured from high frequency to low frequency.

3. The headphone distortion measurement method according to claim 1, characterized in that: The step S21 further comprises: The weight W[f] of each frequency point of the human ear is obtained according to the physiological characteristics of the human ear, and each frequency point of the excitation signal and the response signal is weighted according to the weight W[f], and the energy P of each frequency point after weighted processing is obtained based on the calculation result. w [f]; Simulate the internal noise of the human body and convert the energy P of each frequency point w [f] plus the energy E[f] of the internal noise at each frequency point as the energy Pp[f].

4. The headphone distortion measurement method according to claim 3, characterized in that: The step S3 further comprises: For each step of the response signal after weighting, a step FFT transformation is performed to obtain the spectrum; A tracking filter is used to locate the harmonics of each frequency point, and its high-order harmonic components corresponding to the fundamental frequency are extracted. Based on the high-order harmonic components, the percentage measurement result Rub&Buzz(f) of the harmonic analysis is calculated.

5. The headphone distortion measurement method according to claim 1, characterized in that: Before each frequency point of the excitation signal and the response signal is passed through the psychoacoustic model, the spectrum of the excitation signal and the spectrum of the response signal are horizontally calibrated.

6. The headphone distortion measurement method according to claim 5, characterized in that: The level calibration method further includes: proportionally adjusting the spectrum level of the excitation signal to match the spectrum level of the response signal.

7. The headphone distortion measurement method according to claim 1, characterized in that: The step S4 further comprises: A distortion curve is produced according to the result of multiplying the percentage measurement result Rub&Buzz(f) of each frequency point by the noise loudness NL[k], and a qualified threshold curve is set. If the distortion curve does not exceed the threshold curve, a qualified earphone sound quality result is output; or Get the product of the percentage measurement result Rub&Buzz(f) of each frequency point and the noise loudness NL[k]. If the product result of the frequency point does not exceed the threshold set for the frequency point, the headphone sound quality is qualified.

8. A storage medium, characterized in that The storage medium stores one or more programs, and when the one or more programs are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

9. Headphones, comprising a processor and an audio output device electrically connected to the processor; It is characterized in that It also comprises a memory arranged to store computer executable instructions which, when executed, cause the processor to perform a method according to any one of claims 1 to 7.

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