Noise decibel testing method and device based on smart wearable device

Abstract

A noise decibel testing method and device based on a smart wearable device, comprising: obtaining a PCM value of an audio signal of the smart wearable device and converting the PCM value into an RMS value; converting the RMS value into a voltage value, and then converting the voltage value into a sound pressure value; calculating a noise decibel of the audio signal according to the sound pressure value to obtain a first noise decibel value; obtaining a noise decibel compensation value by fitting a noise compensation curve; and compensating the first noise decibel value based on the noise decibel compensation value to obtain an actual noise decibel value tested by the smart wearable device. According to the audio signal of the smart wearable device, the first noise decibel value is obtained, the noise decibel compensation value is obtained by fitting the noise compensation curve, the first noise decibel value is compensated, and the actual noise decibel value measured by the smart wearable device is obtained, thereby solving the problem that the existing special instrument for testing noise decibel cannot be integrated into a smart watch and thus cannot directly test an external noise decibel value.

Description

Technical Field

[0001] This application relates to smart wearable devices, and more particularly to a method and apparatus for testing noise decibels based on smart wearable devices. Background Technology

[0002] Existing smart wearable devices, such as smartwatches, smart bracelets, and wearable headphones, are equipped with microphone sensors as standard. These microphone sensors are usually only used for Bluetooth calls or recording and do not provide sound pressure level noise decibel testing functions. Existing dedicated instruments for testing noise decibels are limited by their structure and size and cannot be integrated into the aforementioned smart wearable devices, making it difficult for these smart wearable devices to directly test the external noise decibel value. Summary of the Invention

[0003] This application provides a method and apparatus for testing noise decibels based on smart wearable devices.

[0004] According to a first aspect of this application, this application provides a method for testing noise decibels based on a smart wearable device, comprising:

[0005] Acquire the PCM value of the audio signal from the smart wearable device and convert the PCM value into an RMS value;

[0006] The RMS value is converted into a voltage value, and then the voltage value is converted into a sound pressure level.

[0007] The noise decibels of the audio signal are calculated based on the sound pressure value to obtain a first noise decibel value;

[0008] The noise compensation value in decibels is obtained by fitting the noise compensation curve;

[0009] The first noise decibel value is compensated based on the noise decibel compensation value to obtain the actual noise decibel value measured by the smart wearable device.

[0010] In the method involved in this application, acquiring the PCM value of the audio signal from the smart wearable device and converting the PCM value into an RMS value includes:

[0011] Collect the PCM values ​​of the smart wearable device at N data points within time T;

[0012] The PCM values ​​are preprocessed to optimize abnormal change points, resulting in N preprocessed PCM values.

[0013] Based on the preprocessed PCM value, the RMS value is calculated using the following formula:

[0014]

[0015] Where x1, x2, ..., x n Let N be a set of preprocessed PCM values ​​within time T, where n=N.

[0016] In the method involved in this application, the step of preprocessing the PCM value to optimize abnormal change points and obtain N preprocessed PCM values ​​includes:

[0017] Collect the PCM values ​​of the smart wearable device at N data points every time interval T, where N = 125 / 1000*16000;

[0018] Obtain the absolute values ​​of the differences by taking the subtraction values ​​of the preceding and following values, d1, d2, ..., d. m m = N-1;

[0019] For d1, d2 to d m Sort the integers and obtain the difference in rank of the 3rd % * m integer, let's assume its value is d.

[0020] Previous value d i and after d i-1 Compare them; if the absolute value of the difference is greater than d, then d i = (d i +d i-1 ) / 2, otherwise remain unchanged.

[0021] In the method involved in this application, obtaining the noise decibel compensation value by fitting a noise compensation curve includes:

[0022] The noise error parameter equation is set as a univariate polynomial equation.

[0023] The first noise decibel value and the second noise decibel value output by a professional testing instrument are compared using a noise comparison test with an interval of a first preset value. The difference is obtained, and the noise error parameter equation is solved based on the first noise decibel value and the difference.

[0024] The noise compensation value for the first noise decibel value is obtained according to the noise error parameter equation.

[0025] In the method involved in this application, the step of using a noise comparison test with a noise interval of a first preset value to measure the first noise decibel value and the second noise decibel value output by a professional testing instrument, obtaining the difference, and solving the noise error parameter equation based on the first noise decibel value and the difference, includes:

[0026] Suppose there are L tests for comparing the second noise decibel value in the kth group, where L>10. Obtain the first noise decibel value and the difference between the first noise decibel value and the second noise decibel value in L tests. Remove the largest and smallest 10%*L first noise decibel values ​​and the differences. Calculate the average of the remaining 80%*L first noise decibel values ​​to obtain the processed first noise decibel value. Calculate the average of the remaining 80%*L differences to obtain the error of the first noise decibel value.

[0027] Test M groups, obtain the first noise decibel value after processing M groups and the error of the corresponding first noise decibel value, and M is much greater than 7;

[0028] Based on the processed first noise decibel value and the error, the error equation is solved using least squares, and the error magnitude of the fitted noise error parameter equations with different degrees is evaluated using root mean square error. The degree of the noise error parameter equation is determined and the coefficients are solved.

[0029] According to a second aspect of this application, this application provides a noise decibel testing device based on a smart wearable device, comprising:

[0030] An acquisition module is used to acquire the PCM value of the audio signal from the smart wearable device and convert the PCM value into an RMS value;

[0031] A conversion module is used to convert the RMS value into a voltage value, and then convert the voltage value into a sound pressure value;

[0032] The calculation module is used to calculate the noise decibel of the audio signal based on the sound pressure value to obtain a first noise decibel value;

[0033] The processing module is used to obtain the noise compensation value in decibels by fitting the noise compensation curve;

[0034] The compensation module is used to compensate the first noise decibel value based on the noise decibel compensation value to obtain the actual noise decibel value measured by the smart wearable device.

[0035] In the apparatus of this application, the acquisition module includes:

[0036] The acquisition unit is used to acquire the PCM values ​​of N data points of the smart wearable device within time T;

[0037] A preprocessing unit is used to preprocess the PCM values, optimize abnormal change points, and obtain N preprocessed PCM values.

[0038] The acquisition unit is configured to calculate the RMS value based on the preprocessed PCM value using the following formula:

[0039]

[0040] Where x1, x2, ..., x n Let N be a set of preprocessed PCM values ​​within time T, where n=N.

[0041] In the apparatus of this application, the preprocessing unit is further configured to:

[0042] Collect the PCM values ​​of the smart wearable device at N data points every time interval T, where N = 125 / 1000*16000;

[0043] Obtain the absolute values ​​of the differences by taking the subtraction values ​​of the preceding and following values, d1, d2, ..., d. m m = N-1;

[0044] For d1, d2 to d m Sort the integers and obtain the difference in rank of the 3rd % * m integer, let's assume its value is d.

[0045] Previous value d i and after d i-1 Compare them; if the absolute value of the difference is greater than d, then d i = (d i +d i-1 ) / 2, otherwise remain unchanged.

[0046] In the apparatus of this application, the processing module includes:

[0047] The setting unit is used to set the noise error parameter equation as a univariate polynomial equation.

[0048] The first processing unit is used to compare the first noise decibel value and the second noise decibel value output by a professional testing instrument using a noise comparison test with an interval of a first preset value, obtain the difference, and solve the noise error parameter equation based on the first noise decibel value and the difference.

[0049] The second processing unit is used to obtain the noise decibel compensation value of the first noise decibel value according to the noise error parameter equation.

[0050] In the apparatus of this application, the first processing unit is further configured to:

[0051] Suppose there are L tests for comparing the second noise decibel value in the kth group, where L>10. Obtain the first noise decibel value and the difference between the first noise decibel value and the second noise decibel value in L tests. Remove the largest and smallest 10%*L first noise decibel values ​​and the differences. Calculate the average of the remaining 80%*L first noise decibel values ​​to obtain the processed first noise decibel value. Calculate the average of the remaining 80%*L differences to obtain the error of the first noise decibel value.

[0052] Test M groups, obtain the first noise decibel value after processing M groups and the error of the corresponding first noise decibel value, and M is much greater than 7;

[0053] Based on the processed first noise decibel value and the error, the error equation is solved using least squares, and the error magnitude of the fitted noise error parameter equations with different degrees is evaluated using root mean square error. The degree of the noise error parameter equation is determined and the coefficients are solved.

[0054] Due to the adoption of the above technical solutions, the beneficial effects of this application are as follows:

[0055] The noise decibel testing method and apparatus based on smart wearable devices provided in this application include: acquiring the PCM value of the audio signal from the smart wearable device and converting the PCM value into an RMS value; converting the RMS value into a voltage value, and then converting the voltage value into a sound pressure level (SPL) value; calculating the noise decibel of the audio signal based on the SPL value to obtain a first noise decibel value; obtaining a noise compensation value by fitting a noise compensation curve; and compensating the first noise decibel value based on the noise compensation value to obtain the actual external noise decibel value measured by the smart wearable device. This application provides a method and apparatus for directly testing noise decibels using smart wearable devices, such as smartwatches, to obtain a first noise decibel value from the audio signal, then obtains the noise compensation value by fitting a noise compensation curve, and compensates the first noise decibel value to obtain the actual external noise decibel value measured by the smart wearable device. This allows smart wearable devices to directly test noise decibels, solving the problem that existing dedicated instruments for testing noise decibels are limited by their structure and size and cannot be integrated into the aforementioned smart wearable devices, thus preventing direct testing of external noise decibel values. Attached Figure Description

[0056] Figure 1 A flowchart of one implementation of the method provided in this application embodiment;

[0057] Figure 2 A flowchart of another implementation of the method provided in the embodiments of this application;

[0058] Figure 3 A flowchart of another embodiment of the method provided in this application;

[0059] Figure 4 A schematic diagram of program modules in one embodiment of the apparatus provided in this application;

[0060] Figure 5 This is a schematic diagram of the program modules of the detection device according to an embodiment of this application in another implementation. Detailed Implementation

[0061] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings. Similar elements in different embodiments are referred to by associated similar element reference numerals. In the following embodiments, many details are described to facilitate a better understanding of this application. However, those skilled in the art will readily recognize that some features may be omitted in different situations, or may be replaced by other elements, materials, or methods. In some cases, certain operations related to this application are not shown or described in the specification. This is to avoid obscuring the core parts of this application with excessive description. For those skilled in the art, detailed description of these related operations is not necessary; they can fully understand the related operations based on the description in the specification and general technical knowledge in the art.

[0062] Furthermore, the features, operations, or characteristics described in the specification can be combined in any suitable manner to form various embodiments. At the same time, the steps or actions in the method description can be rearranged or adjusted in a manner obvious to those skilled in the art. Therefore, the various orders in the specification and drawings are only for the clear description of a particular embodiment and do not imply a necessary order, unless otherwise stated that a particular order must be followed.

[0063] The serial numbers assigned to components in this article, such as "first" and "second", are used only to distinguish the objects being described and have no sequential or technical meaning.

[0064] Furthermore, the technical features and solutions described herein can be combined in any suitable manner in one or more embodiments. It will be readily understood by those skilled in the art that the steps or order of operations related to the embodiments provided herein can also be changed. Therefore, any order in the drawings and embodiments is for illustrative purposes only and does not imply a requirement to follow a particular order, unless explicitly stated otherwise.

[0065] like Figure 1 As shown in the figure, one embodiment of the noise decibel testing method based on a smart wearable device provided in this application includes the following steps:

[0066] Step 101: Obtain the PCM value of the audio signal from the smart wearable device and convert the PCM value into an RMS value.

[0067] like Figure 2 As shown in the embodiment of this application, the noise decibel testing method based on a smart wearable device includes step 101, which may include the following steps:

[0068] Step 1011: Collect the PCM values ​​of N data points from the smart wearable device within time T;

[0069] Step 1012: Preprocess the PCM values, optimize abnormal change points, and obtain N preprocessed PCM values;

[0070] Step 1013: Based on the preprocessed PCM value, calculate the RMS value using the following formula:

[0071]

[0072] Where x1, x2, ..., x n Let N be a set of preprocessed PCM values ​​within time T, where n = N.

[0073] The noise decibel testing method based on a smart wearable device provided in this application embodiment may further include the following steps in step 1012:

[0074] Collect the PCM values ​​of the smart wearable device at N data points every time interval T, where N = 125 / 1000*16000;

[0075] Obtain the absolute values ​​of the differences by taking the subtraction values ​​of the preceding and following values, d1, d2, ..., d. m m = N-1;

[0076] For d1, d2 to d m Sort the integers and obtain the difference in rank of the 3rd % * m integer, let's assume its value is d.

[0077] Previous value d i and after d i-1 Compare them; if the absolute value of the difference is greater than d, then d i = (d i +d i-1 ) / 2, otherwise remain unchanged.

[0078] Step 102: Convert the RMS value to a voltage value, and then convert the voltage value to a sound pressure level.

[0079] In one implementation, the RMS value can be converted into a voltage value using the following formula.

[0080] V_signal= (RMS / full_scale)*v_signal_m;

[0081] Where V_signal is the calculated voltage value in volts; full_scale is the full-scale PCM value, which is 32768 when the PCM is 16-bit; v_signal_m is the maximum voltage at full scale, provided by the microphone hardware specifications.

[0082] In one implementation, the voltage value can be converted into a sound pressure value using the following formula:

[0083] P_signal = V_signal / Microp_sens;

[0084] Where P_signal is the calculated sound pressure level in Pa (Pascal), and Microp_sens is the microphone sensitivity in V / Pa.

[0085] Step 103: Calculate the noise decibel of the audio signal based on the sound pressure value to obtain the first noise decibel value.

[0086] In one implementation, the first noise decibel value can be calculated using the following formula:

[0087] dB_SPL = 20 * log10(P_signal / P_ref)

[0088] Where dB_SPL is the first noise level in decibels, and P_ref is the reference sound pressure level, which is usually 20e-6pa, where pa is a Pascal.

[0089] Step 104: Obtain the noise compensation value in decibels by fitting the noise compensation curve.

[0090] like Figure 3 As shown in the embodiment of this application, the noise decibel testing method based on a smart wearable device includes step 104, which may include the following steps:

[0091] Step 1041: Set the noise error parameter equation as a univariate polynomial equation;

[0092] Step 1042: Use a noise comparison test with a noise interval of the first preset value to compare the first noise decibel value with the second noise decibel value output by a professional testing instrument, obtain the difference, and solve the noise error parameter equation based on the first noise decibel value and the difference.

[0093] Step 1043: Obtain the noise compensation value of the first noise decibel value according to the noise error parameter equation.

[0094] In one implementation, step 1042 may further include the following steps:

[0095] Suppose there are L tests for comparing the second noise decibel value in the kth group, where L>10. Obtain the first noise decibel value and the difference between the first and second noise decibel values ​​from the L tests. Remove the largest and smallest 10%*L first noise decibel values ​​and their differences. Calculate the average of the remaining 80%*L first noise decibel values ​​to obtain the processed first noise decibel value. Calculate the average of the remaining 80%*L differences to obtain the error of the first noise decibel value.

[0096] Test M groups, obtain the first noise decibel value of the processed M groups and the error of the corresponding first noise decibel value, and M is much greater than 7;

[0097] Based on the processed first noise decibel value and error, the error equation is solved using least squares. The root mean square error is used to evaluate the error magnitude of the fitted noise error parameter equations of different orders. The order of the noise error parameter equations is determined and the coefficients are solved.

[0098] The noise decibels calculated above are sound pressure level decibels. However, due to limitations in the microphone's hardware characteristics and the noise decibels in the real physical world, there are errors, and the relationship between the error and decibels is non-linear.

[0099] Assume the error equation is f(x) = a0 + a1*x + a2*x 2 + ...a n *x n The value of n is undetermined;

[0100] A noise comparison test is conducted using a noise interval of dB_intr (dB_SPL) to compare the first noise value (dB_SPL) with the second noise value (dB_real) output by a professional instrument, and the difference (dB_diff) is obtained. The smaller the value of dB_intr, the better, and it does not need to be fixed.

[0101] (1) Suppose that there are L comparison tests of dB_real in the kth group (L>10), and L dB_SPL and the difference with dB_real are obtained. Remove the maximum and minimum 10%*L dB_SPL and the difference, and calculate the average of the remaining 80%*L dB_SPL and the difference. This average is the processed dB_SPL and the processed dB_diff of the kth group. The processed dB_diff is the error.

[0102] (2) Test group M, obtain the processed dB_SPL and processed dB_diff of group M, and M is much greater than 7.

[0103] (3) Assuming the highest betweenness number n of the error equation is 2, 3, 4, 5, 6, 7, the error equation is solved using least squares, and the root mean square error (RMSE) is used to evaluate the error magnitude of the fitting of different error equations when n takes different values.

[0104]

[0105] in, The noise level in decibels was measured by professional instruments. is the noise value after the first noise decibel value is compensated by the corresponding n-dielectric error equation, and p is the number of test data when evaluating the n-dielectric error equation.

[0106]

[0107] When n takes the values ​​2, 3, ... 7, the RMSE calculates the following results:

[0108] R² = 2.3218

[0109] R3 = 1.9016

[0110] R4 = 1.0531

[0111] R5 = 0.9535

[0112] R6 = 0.9513

[0113] R7 = 0.9507

[0114] Since R5 is smaller than R2 - R4, and while the errors of R6 and R7 are reduced, the performance is not significantly improved. That is, the error of the 5th-degree polynomial is smaller than that of the 2nd, 3rd, and 4th-degree polynomials, and while the errors of the 6th and 7th-degree polynomials are reduced compared to the 5th-degree polynomial, the performance is not significantly improved. Therefore, the error equation can be chosen as the 5th-degree polynomial.

[0115] f(x) = a0 + a1*x + a2*x 2 + a3*x 3 + a4*x 4 + a5*x 5

[0116] And the coefficients of a0-a5 are [3.2193, 1.9135, -0.0829, -0.1943, -0.0264, 0.0020].

[0117] Step 105: Compensate the first noise decibel value based on the noise decibel compensation value to obtain the actual noise decibel value measured by the smart wearable device.

[0118] The actual decibel value of noise measured by smart wearable devices can be calculated using the following formula:

[0119] dB_OUT = dB_SPL + f(dB_SPL)

[0120] Where dB_OUT is the actual decibel value of the noise measured by the smart wearable device.

[0121] like Figure 4 As shown in the embodiment of this application, the noise decibel testing device based on a smart wearable device, in one embodiment, includes an acquisition module 410, a conversion module 420, a calculation module 430, a processing module 440, and a compensation module 450.

[0122] The acquisition module 410 is used to acquire the PCM value of the audio signal from the smart wearable device and convert the PCM value into an RMS value.

[0123] The conversion module 420 is used to convert RMS values ​​into voltage values, and then convert voltage values ​​into sound pressure levels.

[0124] The calculation module 430 is used to calculate the noise decibel of the audio signal based on the sound pressure value to obtain the first noise decibel value.

[0125] The processing module 440 is used to obtain the noise compensation value in decibels by fitting the noise compensation curve.

[0126] The compensation module 450 is used to compensate the first noise decibel value based on the noise decibel compensation value to obtain the actual noise decibel value measured by the smart wearable device.

[0127] like Figure 5 As shown in the embodiment of this application, another implementation of the noise decibel testing device based on a smart wearable device includes an acquisition module 410, a conversion module 420, a calculation module 430, a processing module 440, and a compensation module 450.

[0128] The acquisition module 410 is used to acquire the PCM value of the audio signal from the smart wearable device and convert the PCM value into an RMS value.

[0129] The noise decibel testing device based on a smart wearable device provided in this application embodiment includes a 410 acquisition module, which may include a 411 acquisition unit, a 412 preprocessing unit, and a 413 acquisition unit.

[0130] Acquisition unit 411 is used to acquire the PCM values ​​of N data points of the smart wearable device within time T;

[0131] The preprocessing unit 412 is used to preprocess the PCM values, optimize abnormal change points, and obtain N preprocessed PCM values.

[0132] Acquisition unit 413 is used to calculate the RMS value based on the preprocessed PCM value using the following formula:

[0133]

[0134] Where x1, x2, ..., x n Let N be a set of preprocessed PCM values ​​within time T, where n=N.

[0135] In one embodiment, the preprocessing unit 412 can also be used for:

[0136] The PCM values ​​of the smart wearable device are collected at N data points every time interval T, where N = 125 / 1000*16000;

[0137] Obtain the absolute values ​​of the differences by taking the subtraction values ​​of the preceding and following values, d1, d2, ..., dn. m m = N-1;

[0138] For d1, d2 to d m Sort the integers and obtain the difference in rank of the 3rd % * m integer, let's assume its value is d.

[0139] Previous value d i and after d i-1 Compare them; if the absolute value of the difference is greater than d, then d i = (d i +d i-1 ) / 2, otherwise remain unchanged.

[0140] The conversion module 420 is used to convert RMS values ​​into voltage values, and then convert voltage values ​​into sound pressure levels.

[0141] In one implementation, the RMS value can be converted into a voltage value using the following formula.

[0142] V_signal= (RMS / full_scale)*v_signal_m;

[0143] Where V_signal is the calculated voltage value in volts; full_scale is the full-scale PCM value, which is 32768 when the PCM is 16-bit; v_signal_m is the maximum voltage at full scale, provided by the microphone hardware specifications.

[0144] In one implementation, the voltage value can be converted into a sound pressure value using the following formula:

[0145] P_signal = V_signal / Microp_sens;

[0146] Where P_signal is the calculated sound pressure level in Pa (Pascal), and Microp_sens is the microphone sensitivity in V / Pa.

[0147] The calculation module 430 is used to calculate the noise decibel of the audio signal based on the sound pressure value to obtain the first noise decibel value.

[0148] In one implementation, the first noise decibel value can be calculated using the following formula:

[0149] dB_SPL = 20 * log10(P_signal / P_ref)

[0150] Where dB_SPL is the first noise level in decibels, and P_ref is the reference sound pressure level, which is usually 20e-6pa, where pa is a Pascal.

[0151] The processing module 440 is used to obtain the noise compensation value in decibels by fitting the noise compensation curve.

[0152] The noise decibel testing device based on a smart wearable device provided in this application embodiment, the processing module 440, may include a setting unit 441, a first processing unit 442, and a second processing unit 443.

[0153] Setting unit 441 is used to set the noise error parameter equation as a univariate polynomial equation.

[0154] The first processing unit 442 is used to compare a first noise decibel value with a second noise decibel value output by a professional testing instrument using a noise comparison test with an interval of a first preset value, obtain the difference, and solve the noise error parameter equation based on the first noise decibel value and the difference.

[0155] The second processing unit 443 is used to obtain the noise decibel compensation value of the first noise decibel value according to the noise error parameter equation.

[0156] In one embodiment, the first processing unit 442 can also be used for:

[0157] Suppose there are L tests for comparing the second noise decibel value in group k, where L>10. Obtain the first noise decibel value and the difference between the first and second noise decibel values ​​from L tests. Remove the largest and smallest 10%*L first noise decibel values ​​and their differences. Calculate the average of the remaining 80%*L first noise decibel values ​​to obtain the processed first noise decibel value for group K. Calculate the average of the remaining 80%*L differences to obtain the error of the first noise decibel value.

[0158] Test M groups, obtain the first noise decibel value of the processed M groups and the error of the corresponding first noise decibel value, and M is much greater than 7;

[0159] Based on the processed first noise decibel value and error, the error equation is solved using least squares. The root mean square error is used to evaluate the error magnitude of the fitted noise error parameter equations of different orders. The order of the noise error parameter equations is determined and the coefficients are solved.

[0160] The noise decibels calculated above are sound pressure level decibels. However, due to limitations in the microphone's hardware characteristics and the noise decibels in the real physical world, there are errors, and the relationship between the error and decibels is non-linear.

[0161] Assume the error equation is f(x) = a0 + a1*x + a2*x 2 + ...a n *x n The value of n is undetermined;

[0162] A noise comparison test is conducted using a noise interval of dB_intr (dB_SPL) to compare the first noise value (dB_SPL) with the second noise value (dB_real) output by a professional instrument, and the difference (dB_diff) is obtained. The smaller the value of dB_intr, the better, and it does not need to be fixed.

[0163] (1) Suppose that there are L comparison tests of dB_real in the kth group (L>10), and L dB_SPL and the difference with dB_real are obtained. Remove the largest and smallest 10%*L dB_SPL and the difference, and calculate the average of the remaining 80%*L dB_SPL and the difference. This average is the processed dB_SPL and the processed dB_diff of the kth group. The processed dB_diff is the error.

[0164] (2) Test group M, obtain the processed dB_SPL and processed dB_diff of group M, and M is much greater than 7.

[0165] (3) Assuming the highest betweenness number n of the error equation is 2, 3, 4, 5, 6, 7, the error equation is solved using least squares, and the root mean square error (RMSE) is used to evaluate the error magnitude of the fitting of different error equations when n takes different values.

[0166]

[0167] in, The noise level in decibels was measured using professional instruments. is the noise value after the first noise decibel value is compensated by the corresponding n-dielectric error equation, and p is the number of test data when evaluating the n-dielectric error equation.

[0168]

[0169] When n takes the values ​​2, 3, ... 7, the RMSE calculates the following results:

[0170] R² = 2.3218

[0171] R3 = 1.9016

[0172] R4 = 1.0531

[0173] R5 = 0.9535

[0174] R6 = 0.9513

[0175] R7 = 0.9507

[0176] Since R5 is smaller than R2 - R4, and while the errors of R6 and R7 are reduced, the performance is not significantly improved. That is, the error of the 5th-degree polynomial is smaller than that of the 2nd, 3rd, and 4th-degree polynomials, and while the errors of the 6th and 7th-degree polynomials are reduced compared to the 5th-degree polynomial, the performance is not significantly improved. Therefore, the error equation can be chosen as the 5th-degree polynomial.

[0177] f(x) = a0 + a1*x + a2*x 2 + a3*x 3 + a4*x 4 + a5*x 5

[0178] And the coefficients of a0-a5 are [3.2193, 1.9135, -0.0829, -0.1943, -0.0264, 0.0020].

[0179] The compensation module 450 is used to compensate the first noise decibel value based on the noise decibel compensation value to obtain the actual noise decibel value measured by the smart wearable device.

[0180] The actual decibel value of noise measured by smart wearable devices can be calculated using the following formula:

[0181] dB_OUT = dB_SPL + f(dB_SPL)

[0182] Where dB_OUT is the actual decibel value of the noise measured by the smart wearable device.

[0183] Those skilled in the art will understand that all or part of the steps of the various methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: read-only memory, random access memory, disk or optical disk, etc.

[0184] The above description, in conjunction with specific embodiments, provides a further detailed explanation of this application and should not be construed as limiting the specific implementation of this application to these descriptions. Those skilled in the art to which this application pertains can make several simple deductions or substitutions without departing from the concept of this application.

Claims

1. A smart wearable device-based noise decibel test method, characterized by, The method comprises the following steps: obtaining the PCM value of the audio signal of the smart wearable device and converting the PCM value into the RMS value; converting the RMS value into the voltage value, and then converting the voltage value into the sound pressure value; calculating the noise decibel of the audio signal according to the sound pressure value to obtain the first noise decibel value; obtaining the noise decibel compensation value by fitting the noise compensation curve; compensating the first noise decibel value based on the noise decibel compensation value to obtain the actual noise decibel value measured by the smart wearable device.

2. The method of claim 1, wherein, The method for obtaining the PCM value of the audio signal of the smart wearable device and converting the PCM value into the RMS value comprises the following steps: collecting the PCM value of N data points of the smart wearable device within a time T; preprocessing the PCM value to optimize the abnormal change point to obtain N preprocessed PCM values; calculating the RMS value based on the preprocessed PCM value by the following formula: wherein x1, x2,... x n is a set of pre-processed PCM values over T time, n = N.

3. The method of claim 2, wherein, The method for preprocessing the PCM value to optimize the abnormal change point to obtain N preprocessed PCM values comprises the following steps: collecting the PCM value of N data points of the smart wearable device every time T, and N = 125 / 1000*16000; The absolute values of the differences d1, d2,..., d are obtained by sequentially subtracting the previous value from the subsequent value. m , m = N - 1; For d1, d2 to d m Sort, get the difference of the 3rd m integer ranking, assuming its value is d, previous value d i and later d i-1 Compare, if the difference is greater than d, then d i = (d i +d i-1 ) / 2, otherwise keep it unchanged.

4. The method of claim 1, wherein, The method for obtaining the noise decibel compensation value by fitting the noise compensation curve comprises the following steps: setting the noise error parameter equation as a monomial equation; using the noise with the interval size of the first preset value to test the first noise decibel value and the second noise decibel value output by the professional testing instrument, obtaining the difference value, and solving the noise error parameter equation according to the first noise decibel value and the difference value; obtaining the noise decibel compensation value of the first noise decibel value according to the noise error parameter equation.

5. The method of claim 4, wherein, The method for using the noise with the interval size of the first preset value to test the first noise decibel value and the second noise decibel value output by the professional testing instrument, obtaining the difference value, and solving the noise error parameter equation according to the first noise decibel value and the difference value comprises the following steps: assuming that the second noise decibel value of the kth group is tested for L times, L > 10, obtaining L times of the first noise decibel value and the difference value between the first noise decibel value and the second noise decibel value, removing 10%*L of the first noise decibel value and the difference value with the largest and smallest values, obtaining the average value of the remaining 80%*L of the first noise decibel value to obtain the processed first noise decibel value, and obtaining the average value of the remaining 80%*L of the difference value to obtain the error of the first noise decibel value; testing M groups to obtain M groups of the processed first noise decibel value and the corresponding error of the first noise decibel value, and M is much greater than 7; based on the processed first noise decibel value and the error, solving the error equation by using the least square method, evaluating the error size of the noise error parameter equation fitted by the number of times by using the root mean square error, determining the number of times of the noise error parameter equation, and solving the coefficient.

6. A noise decibel testing device based on a smart wearable device, characterized in that, The method comprises the following steps: an obtaining module is configured to obtain the PCM value of the audio signal of the smart wearable device and convert the PCM value into the RMS value; The conversion module is configured to convert the RMS value into a voltage value, and convert the voltage value into a sound pressure value; The calculation module is configured to calculate a noise decibel of the audio signal according to the sound pressure value, and obtain a first noise decibel value; The processing module is configured to obtain a noise decibel compensation value by fitting a noise compensation curve; The compensation module is configured to compensate the first noise decibel value based on the noise decibel compensation value, and obtain an actual noise decibel value measured by the smart wearable device.

7. The apparatus of claim 6, wherein, The acquisition module includes: The acquisition unit is configured to acquire the PCM values of N data points of the smart wearable device within a time T; The preprocessing unit is configured to preprocess the PCM values to optimize abnormal change points, and obtain N preprocessed PCM values; The acquisition unit is configured to calculate the RMS value based on the preprocessed PCM values according to the following formula: wherein x1, x2...x n is a set of pre-processed PCM values over T time, n = N.

8. The apparatus of claim 7, wherein, The preprocessing unit is further configured to: Acquire the PCM values of N data points of the smart wearable device every time T, and N = 125 / 1000*16000; The absolute values of the differences d1, d2,..., d are obtained by sequentially subtracting the previous value from the subsequent value. m , m = N - 1; For d1, d2 to d m Sort, get the difference of the 3rd m integer ranking, assuming its value is d, previous value d i and later d i-1 compare, if the absolute value of the difference is greater than d then d i = (d i +d i-1 ) / 2, otherwise keep it unchanged.

9. The apparatus of claim 6, wherein, The processing module includes: The setting unit is configured to set a noise error parameter equation as a monomial equation; The first processing unit is configured to use noise with a first preset interval size to test the first noise decibel value and a second noise decibel value output by a professional testing instrument, obtain a difference value, and solve the noise error parameter equation according to the first noise decibel value and the difference value; The second processing unit is configured to obtain the noise decibel compensation value of the first noise decibel value according to the noise error parameter equation.

10. The apparatus of claim 9, wherein, The first processing unit is further configured to: Suppose that the second noise decibel value of the kth group is tested L times, L > 10, L times of the first noise decibel value and the difference value between the first noise decibel value and the second noise decibel value are obtained, 10%*L of the maximum and minimum first noise decibel values and the difference values are removed, the average of the remaining 80%*L first noise decibel values is obtained as the processed first noise decibel value, and the average of the remaining 80%*L difference values is obtained as the error of the first noise decibel value; M groups are tested, M groups of processed first noise decibel values and corresponding errors of the first noise decibel values are obtained, and M is much greater than 7; Based on the processed first noise decibel values and the errors, a least square error equation is solved, a root mean square error is used to evaluate the error size of noise error parameter equations with different numbers of times, the number of times of the noise error parameter equation is determined, and the coefficient is solved.

Images