Abnormal sound detection method, active noise reduction system and control method therefor, program product

Abstract

The present disclosure provides an abnormal sound detection method, an active noise reduction system and a control method thereof, a program product and a medium, the abnormal sound detection method comprising: collecting a first noise signal during operation of the active noise reduction system; performing octave calculation on the first noise signal to determine a first segmentation strategy matched with the first noise signal; designing a band-pass filter according to the first segmentation strategy, and filtering the noise signal by using the band-pass filter to obtain at least three signal segments; calculating the sound pressure values of the signal segments, and detecting abnormal sound according to the sound pressure values. The present disclosure determines the segmentation strategy of the noise signal based on octave, divides the noise signal into multiple frequency segments by using the band-pass filter, calculates the sound pressure values of the frequency segments, compares the sound pressure values of the frequency segments to detect abnormal sound, and can effectively and accurately detect abnormal sound. On the other hand, by comparing the sound pressure values of the frequency segments, Fourier transform is not needed, the system calculation power is reduced, and the accuracy is improved.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of noise reduction, and in particular to a abnormal sound detection method, an active noise reduction system and a control method thereof, a program product and a medium. BACKGROUND

[0002] In the active noise reduction system, the noise reduction system utilizes the superimposed interference effect of the loudspeaker control signal (anti-phase noise) generated according to the input signal (including the noise signal collected and the feedback signal) and the noise signal to achieve the reduction of the noise signal, so as to achieve the purpose of noise reduction. However, in this process, when the feedback signal is greater than the noise signal, a howling phenomenon will occur. Even if the feedback signal is less than the noise signal, when the ratio of the feedback signal to the noise signal is greater than a threshold, some stable single-frequency abnormal sound will still be emitted. The amplitude of the stable single-frequency abnormal sound is much smaller than the noise when howling occurs, and there is no obvious difference between the amplitude of the loudspeaker control signal generated when the stable single-frequency abnormal sound occurs and the amplitude of the loudspeaker control signal generated when the system is normally working. Therefore, the traditional howling monitoring scheme based on the amplitude of the loudspeaker control signal in the prior art cannot effectively identify this stable single-frequency abnormal sound phenomenon. SUMMARY

[0003] The technical problem to be solved by the present disclosure is to overcome the defect that the prior art cannot effectively identify the abnormal sound of the active noise reduction system, and to provide an abnormal sound detection method, an active noise reduction system and a control method thereof, a program product and a medium. The present disclosure solves the above technical problem by the following technical scheme:

[0004] In a first aspect, an abnormal sound detection method is provided, which is applied to an active noise reduction system, and the active noise reduction system includes a loudspeaker and a microphone. The abnormal sound detection method includes:

[0005] A first noise signal is collected during the operation of the active noise reduction system, and the first noise signal includes a noise signal of a noise source collected by the microphone and / or a control signal of the loudspeaker;

[0006] Octave calculation is performed on the first noise signal to determine a first segmentation strategy matched with the first noise signal;

[0007] A bandpass filter is designed according to the first segmentation strategy, and the first noise signal is filtered by the bandpass filter to obtain at least three signal segments;

[0008] According to the characteristics of the at least three signal segments, abnormal sound detection is performed on the signal segments of the active noise reduction system.

[0009] Optionally, the octave calculation performed on the first noise signal to determine the segmentation strategy matched with the first noise signal includes:

[0010] octave computing the first noise signal, determining a center frequency of an octave of the first noise signal;

[0011] determining an upper limit frequency and a lower limit frequency contained in the first segmentation strategy according to the center frequency.

[0012] Optionally, the determining the upper limit frequency and the lower limit frequency contained in the segmentation strategy according to the center frequency comprises:

[0013] in the case of octave computing the first noise signal by 1 / 1 octave, the upper limit frequency and the lower limit frequency are calculated by the following formula:

[0014]

[0015] wherein the center frequency of the octave of the first noise signal is represented as f c1 , the upper limit frequency is represented as f high1 , and the lower limit frequency is represented as f low1 .

[0016] or, in the case of octave computing the first noise signal by 1 / 3 octave, the upper limit frequency and the lower limit frequency are calculated by the following formula:

[0017] f high2 = f c2 × 2 1 / 6 , f low2 = f c2 / 2 1 / 6 .

[0018] wherein the center frequency of the 1 / 3 octave of the first noise signal is represented as f c2 , the upper limit frequency is represented as f high2 , and the lower limit frequency is represented as f low2 .

[0019] Optionally, according to the characteristics of the at least three signal segments, the active noise reduction system is subjected to abnormal sound detection, comprising:

[0020] in response to the characteristics meeting the abnormal sound judgment condition, it is determined that the active noise reduction system has stable single-frequency abnormal sound;

[0021] the abnormal sound judgment condition comprises at least one of the following:

[0022] there is a signal segment in the first noise signal with a power ratio greater than or equal to the ratio threshold; the characteristics include the power ratio; the power ratio is the ratio of the power of the signal segment to the total power, and the total power is the sum of the powers of each signal segment;

[0023] the total power is less than a first difference threshold value from a maximum power of the signal segment; the feature comprises a power of the signal segment;

[0024] the total power is within a preset power range; wherein a lower limit value of the preset power range is greater than a maximum power of the noise signal of the noise source, and an upper limit value of the preset power range is less than a power of the first noise signal when the active noise reduction system generates howling;

[0025] a difference between the total power and a filtered power is greater than or equal to a second difference threshold value; the filtered power is a result of filtering out the maximum power of the noise signal from the total power;

[0026] there is a signal segment in the first noise signal, a power difference of which from an adjacent signal segment is greater than or equal to a third difference threshold value.

[0027] Optionally, the feature comprises a sound pressure value; and the whistle detection on the active noise reduction system comprises:

[0028] when the sound pressure value meets a whistle condition, it is determined that the active noise reduction system has a stable single-frequency whistle;

[0029] wherein the whistle condition comprises at least one of:

[0030] P i -P i-1 ≥T1, and P i -P i+1 ≥T1, wherein T1 is a first threshold value, P i is a sound pressure value of the i-th signal segment, P i-1 and P i+1 are sound pressure values of adjacent signal segments of P i ,

[0031] P i -P i-1 ≥T1, P i -P i+1 <T1, and P i+1 -P i+2 ≥T1, wherein P i-1 and P i+1 are sound pressure values of adjacent signal segments of P i , i and P i+2 are sound pressure values of adjacent signal segments of P i+1 , it is determined that the active noise reduction system has a stable single-frequency whistle;

[0032] P-P i <T2, wherein T2 is a second threshold value.

[0033] Optionally, the sound pressure values of the signal segments are calculated, comprising:

[0034] The amplitude of each signal segment is calculated by a root mean square effective value to obtain the power in each signal segment;

[0035] The sound pressure values of the signal segments are calculated by A-weighting the power.

[0036] Optionally, before the step of collecting the first noise signal during the operation of the active noise reduction system, further comprising:

[0037] In the case that the active noise reduction system is not started, a second noise signal is obtained; the second noise signal is a noise signal of a noise source collected by the microphone;

[0038] The octave calculation is performed on the second noise signal to determine a second segmentation strategy matched with the second noise signal;

[0039] The second noise signal is segmented according to the second segmentation strategy, and the characteristic values of the signal segments of the second noise signal are calculated;

[0040] The target gain of a first target signal segment with a characteristic value greater than a characteristic value threshold is determined; the characteristic value of the first target signal segment after filtering the first target signal segment by a band-pass filter with the target gain is less than or equal to the characteristic value threshold;

[0041] The band-pass filter is designed according to the first segmentation strategy, comprising: the gain of the band-pass filter of a second target signal segment is the target gain; wherein the frequency band of the second target signal segment contains the frequency band of the first target signal segment.

[0042] Optionally, before the step of collecting the first noise signal during the operation of the active noise reduction system, further comprising:

[0043] In the case that the active noise reduction system is not started, a second noise signal is obtained; the second noise signal is a noise signal of a noise source collected by the microphone;

[0044] The octave calculation is performed on the second noise signal to determine a second segmentation strategy matched with the second noise signal;

[0045] The second noise signal is segmented according to the second segmentation strategy, and the characteristic values of the signal segments of the second noise signal are calculated;

[0046] The target gain of a first target signal segment with a characteristic value greater than a characteristic value threshold is determined; the characteristic value of the first target signal segment after filtering the first target signal segment by a band-pass filter with the target gain is less than or equal to the characteristic value threshold;

[0047] designing a band-pass filter according to the first segmentation strategy, comprising: designing a frequency band of the band-pass filter according to a center frequency, an upper limit frequency and a lower limit frequency contained in the first segmentation strategy, and designing a gain of the band-pass filter of a second target signal segment according to the target gain; wherein the frequency band of the second target signal segment contains the frequency band of the first target signal segment.

[0048] In a second aspect, a control method of an active noise reduction system is provided, and the control method comprises:

[0049] detecting the abnormal sound of the active noise reduction system according to the abnormal sound detection method of any one of the above aspects;

[0050] in response to the occurrence of the stable single-frequency abnormal sound of the active noise reduction system, restarting the active noise reduction system.

[0051] In a third aspect, an active noise reduction system is provided, and the active noise reduction system comprises: a loudspeaker, a microphone, and a processor; the processor is connected to the loudspeaker and the microphone respectively, and the processor implements the method of any one of the above aspects when executing a computer program.

[0052] In a fourth aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method of any one of the above aspects when executing the computer program.

[0053] In a fifth aspect, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program, and the computer program is executable on a processor to implement the method of any one of the above aspects.

[0054] In a sixth aspect, a computer program product is provided, comprising a computer program, and the computer program is executable on a processor to implement the abnormal sound detection method of any one of the above aspects.

[0055] On the basis of common sense in the art, the above-mentioned preferred conditions can be combined arbitrarily, i.e. to obtain each preferred example of the present disclosure.

[0056] The positive progress effect of the present disclosure is that the segmentation strategy of the noise signal based on the octave determines the frequency distribution of the noise signal power, and through the band-pass filter, the noise signal is divided into multiple signal segments, the characteristics of each signal segment are determined, and the characteristics of each signal segment are compared to detect the abnormal sound, which can effectively and accurately detect the howling and the stable single-frequency abnormal sound. On the other hand, through the comparison of the characteristics of each signal segment, Fourier transform is not needed, which reduces the system computing power while improving the accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1A flow chart of a method for detecting abnormal sound provided for an exemplary embodiment of the present disclosure;

[0058] Figure 2a A frequency domain graph of a normal operation, a howling, and a stable single-frequency abnormal sound produced by an active noise reduction system provided for an exemplary embodiment of the present disclosure;

[0059] Figure 2b A time domain graph of a normal operation, a howling, and a stable single-frequency abnormal sound produced by an active noise reduction system provided for an exemplary embodiment of the present disclosure;

[0060] Figure 3 A time domain signal graph of a signal collected by a reference microphone provided for an exemplary embodiment of the present disclosure;

[0061] Figure 4 A noise signal frequency band graph based on an octave provided for an exemplary embodiment of the present disclosure;

[0062] Figure 5 A noise signal frequency band graph based on a one-third octave provided for an exemplary embodiment of the present disclosure;

[0063] Figure 6 A flow chart of a method for calculating sound pressure values of each signal segment provided for an exemplary embodiment of the present disclosure;

[0064] Figure 7 Another noise signal frequency band graph based on a one-third octave provided for an exemplary embodiment of the present disclosure;

[0065] Figure 8 A flow chart of a control method of an active noise reduction system provided for an exemplary embodiment of the present disclosure;

[0066] Figure 9 A structural schematic diagram of an electronic device shown for an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0067] The present disclosure will be further described by way of examples without limiting the present disclosure to the described examples.

[0068] The prefix words such as “first”, “second” in the embodiments of the present disclosure are merely used to distinguish different description objects, and have no limiting effect on the position, order, priority, quantity or content of the described objects. The use of ordinal words such as ordinal words in the embodiments of the present disclosure does not constitute a limitation on the described objects, and the description of the described objects should refer to the description in the context of the claims or embodiments, and should not constitute an additional limitation because of the use of such prefix words. In addition, in the description of the embodiments, unless otherwise stated, the meaning of “a plurality of” is two or more.

[0069] Figure 1 The flowchart illustrates an abnormal noise detection method provided in an exemplary embodiment of this disclosure. The abnormal noise detection method is applied to an active noise cancellation system, which includes components such as a speaker, a microphone, and a controller.

[0070] The abnormal noise detected in this embodiment refers to a noise emitted by the speaker of the active noise cancellation system due to certain malfunctions or interference during operation, which affects the user experience. Abnormal noise includes howling and stable single-frequency abnormal noise (or stable single-frequency sound).

[0071] The characteristics of howling during active noise cancellation are: see Figure 2a and Figure 2b The noise spectrum exhibits a prominent peak, with the amplitude at its highest point significantly higher than adjacent frequencies. This peak is generally much larger than the peak of a stable single-frequency abnormal sound, accounting for a large portion of the total noise power. From the moment it is generated, the power amplitude in the time domain gradually increases, with the ratio of maximum power to minimum power generally exceeding 5 times, and the amplitude typically approaching or even exceeding the maximum output power of the loudspeaker itself. Figure 2a The second to fifth columns of the table refer to the amplitude (power) at 94.28Hz, 108.75Hz, 0Hz and 6000Hz respectively, and the sixth column, RMS data, refers to the root mean square value of the amplitude from 0 to 6000Hz.

[0072] Unlike howling, stable single-frequency abnormal noise / stable single-frequency sound is characterized by a prominent peak in the noise spectrum. The amplitude of the peak (noise peak value) is higher than the amplitude of the corresponding adjacent frequency, and the power of this peak accounts for a relatively small proportion of the total noise power. That is, the power of the peak accounts for a proportion of the total noise power that is less than or equal to the power threshold, where the power threshold is determined based on experimental data. Figure 2a As shown by the blue curve, the bandwidth of this peak does not exceed 50Hz. See also Figure 2b In stable single-frequency noise, the power amplitude in the time domain is relatively stable from the beginning of its generation, with little power variation. The ratio of the maximum power to the minimum power will not exceed 3 times, and the amplitude is generally much smaller than the maximum sound power of the speaker itself. Figure 2b The left vertical axis represents the power amplitude, and the right vertical axis represents the sound pressure amplitude; the two can be converted to each other.

[0073] This disclosure primarily detects whether a stable single-frequency abnormal sound occurs in the active noise cancellation system. (See also...) Figure 1 The abnormal sound detection method includes:

[0074] Step 101: Acquire the first noise signal during the operation of the active noise cancellation system.

[0075] The first noise signal can be a noise signal emitted by a noise source collected by the first microphone. The noise signal emitted by the noise source is used as the basis data for the detection of the abnormal sound. The first microphone is arranged near the noise source. It should be noted that the first noise signal can be the collection result of one first microphone or the combination of the collection results of multiple first microphones. At this time, the first noise signal collected by the first microphone is the superposition result of the original noise signal emitted by the noise source and the feedback signal.

[0076] The active noise reduction system can be used for noise reduction of electric appliances such as range hoods and washing machines. Taking the noise reduction of the range hood as an example, the noise source can be the fan of the range hood, and the microphone (first microphone) for collecting the noise signal is arranged near the fan.

[0077] The first noise signal can be a control signal of a loudspeaker, and the control signal is used as the basis data for the detection of the abnormal sound. It should be noted that the control signal of the loudspeaker can be directly obtained by acquiring the output result of the controller, or can be collected by the second microphone arranged near the loudspeaker included in the active noise reduction system. The first noise signal can be the control signal of one loudspeaker or the combination of the control signals of multiple loudspeakers.

[0078] The first noise signal can also be a combination of the noise signal of the noise source and the control signal of the loudspeaker. The specific combination manner is not particularly limited in the embodiments of the present disclosure.

[0079] Since the frequency band range in which the abnormal sound often occurs is 20Hz-3000Hz, the first noise signal with the frequency band of 20Hz-3000Hz can be collected in step 100. Therefore, for signals not in this frequency band range, no calculation is needed, which can reduce the calculation amount and improve the rate of abnormal sound detection.

[0080] In step 102, octave calculation is performed on the first noise signal to determine a first segmentation strategy matched with the first noise signal.

[0081] Specifically, octave calculation is performed on the first noise signal, and the first noise signal is divided into several octave frequency bands, so that the first noise signal is continuously partitioned. Different octave calculations will divide the first noise signal into different numbers of signal segments. The finer the division, the higher the accuracy, but the higher the corresponding computing power requirement. Different segmentation strategies can be adopted for different first noise signals.

[0082] In step 103, a band-pass filter is designed according to the first segmentation strategy, and the band-pass filter is used to filter the noise signal to obtain at least three signal segments.

[0083] Specifically, according to the octave band determined in step 102, a band-pass filter is designed. Each band-pass filter has the same function, which is to allow only a certain frequency range of waves to pass, but each band-pass filter allows different frequencies to pass. The number of band-pass filters is determined based on the partitioning result after octave calculation. In the heterophonic judgment, at least three signal segments are involved, so the band-pass filter filters the first noise signal to obtain a number of signal segments of more than 3.

[0084] Step 104, detecting heterophony of the active noise reduction system according to the characteristics of the at least three signal segments.

[0085] In this embodiment, the segmentation strategy of the noise signal is determined based on the octave, which can finely analyze the frequency distribution of the noise signal power. By dividing the noise signal into multiple signal segments through the band-pass filter, the characteristics of each signal segment are determined, and the characteristics of each signal segment are compared to detect heterophony, which can effectively and accurately detect howling and stable single-frequency heterophony. On the other hand, by comparing the characteristics of each signal segment, Fourier transform is not required, which reduces the system computing power while improving accuracy.

[0086] In one embodiment, the first noise signal is subjected to octave calculation to determine a first segmentation strategy matched with the first noise signal, comprising:

[0087] The octave calculation of the first noise signal determines the center frequency of the octave of the first noise signal, and the upper limit frequency and the lower limit frequency contained in the first segmentation strategy are determined according to the center frequency.

[0088] Specifically, in the octave calculation of the first noise signal, the center frequency of the first noise signal needs to be determined first, and then the upper limit frequency and the lower limit frequency contained in the first segmentation strategy are determined according to the center frequency.

[0089] In one embodiment, the upper limit frequency and the lower limit frequency contained in the first segmentation strategy are determined according to the center frequency, comprising:

[0090] In the case of using 1 / 1 octave to calculate the octave of the first noise signal, the upper limit frequency and the lower limit frequency are calculated by the following formula: Wherein, the center frequency of the octave of the first noise signal is represented as f c1 , the upper limit frequency is represented as f high1 , and the lower limit frequency is represented as f low1 .

[0091] Specifically, the center frequency f c1The octave standard center frequencies 31.5, 63, 125, 250, 500, 1000, 2000, 4000, 8000, 16000 can be used. The upper limit frequency of the corresponding octave is determined according to the calculation formula, Lower limit frequency For example, the frequency band with the center frequency of 1000 Hz, the center frequency f c1 = 1000 Hz, the upper limit frequency f Lower limit frequency As Figure 4 The frequency band diagram of the first noise signal based on octave is shown in the figure, the horizontal coordinate represents the frequency, each rectangle represents a different frequency band, and the corresponding vertical coordinate value represents the power of the frequency band. The total power of these frequency bands is 80.56db, the sound pressure value / dB of the frequency band with the center frequency of 63hz is 66.03db, the sound pressure value / dB of the frequency band with the center frequency of 125hz is 79.65db, and the sound pressure value / dB of the frequency band with the center frequency of 250hz is 63.02db. Here, the frequency band of the abnormal sound is in the frequency band with the center frequency of 125hz.

[0092] In one embodiment, when the octave calculation of the first noise signal is performed using 1 / 3 octave, the upper limit frequency and the lower limit frequency are calculated by the following formula: f high2 = f c2 × 2 1 / 6 , f low2 = f c2 / 2 1 / 6 , wherein the center frequency of the noise signal 1 / 3 octave is represented as f c2 , the upper limit frequency is represented as f high2 , and the lower limit frequency is represented as f low2 .

[0093] Specifically, the center frequency f c2 of the noise signal is determined, and the one-third octave standard center frequencies 20, 25, 31.5, 40, 50, 63, 80, 100, 125, 160, 200, 250, 315, 400, 500, 630, 800, 1000, 1250, 1600, 2000, 2500, 3150, 4000, 5000, 6300, 8000, 10000, 12500, 16000, 20000 can be used. The upper limit frequency f high = f c2 × 2 1 / 6 , and the lower limit frequency f low = f c2 / 2 1 / 6For example, the frequency band with a center frequency of 1000 Hz, then the center frequency: f c2 = 1000 Hz, the upper limit frequency f high = f c2 / 3 1 / 6 ≈ 1122.46 Hz, the lower limit frequency f low = f c2 / 2 1 / 6 ≈ 891.25 Hz. As Figure 5 shown in Figure 1 is a frequency band diagram of the first noise signal based on one-third octave, the horizontal coordinate represents the frequency, and each rectangle represents a different frequency band. The corresponding vertical coordinate value represents the power of the frequency band. Mono 80.56db represents the total power of these frequency bands is 80.56db, the sound pressure value / dB of the frequency band with a center frequency of 80hz is 57.23db, the sound pressure value / dB of the frequency band with a center frequency of 100hz is 79.31db, and the sound pressure value / dB of the frequency band with a center frequency of 125hz is 67.94db. Here, the frequency band where the abnormal sound is located is within the frequency band with a center frequency of 100hz.

[0094] In this embodiment, 1 / 3 octave is selected to determine the division of the frequency band, which can more finely analyze the frequency distribution of the noise power and study the distribution of the signal power in different frequency bands. From the frequency distribution analysis, 1 / 3 octave is mainly used to analyze the frequency distribution of the noise power, and the division of each frequency band is realized by using a band-pass filter. This analysis method can provide more detailed frequency resolution, so as to more accurately understand the performance of the noise at different frequencies. From the signal power analysis, 1 / 3 octave analysis not only focuses on the frequency distribution of the noise, but also emphasizes the study of the distribution of the signal power in different frequency bands. This analysis method helps to understand the intensity and influence of the signal at different frequencies, and is of great significance for evaluating and optimizing the signal processing system.

[0095] In this embodiment, the segmentation strategy of the noise signal is determined by octave and 1 / 3 octave for subsequent abnormal sound detection. The octave frequency band is divided less, reducing the requirement for computing power, while the 1 / 3 octave frequency band is divided more finely, corresponding to a high requirement for computing power, but with high accuracy. In actual use, different octaves can be selected to segment the noise signal according to specific circumstances.

[0096] The following describes several specific implementation modes of abnormal sound detection.

[0097] In one embodiment, in response to the power meeting the abnormal sound judgment condition, it is determined that the active noise reduction system has a stable single-frequency abnormal sound;

[0098] Specifically, the abnormal sound judgment condition includes at least one of the following:

[0099] (1) the power ratio of a signal segment in the first noise signal is greater than or equal to a ratio threshold; the feature includes the power ratio; the power ratio is the ratio of the power of the signal segment to the total power, and the total power is the sum of the powers of the signal segments;

[0100] (2) the difference between the total power and the maximum power of the signal segment is less than a first difference threshold; the feature includes the power of the signal segment;

[0101] (3) the total power is within a preset power range; the lower limit of the preset power range is greater than the maximum power of the noise signal of the noise source, and the upper limit of the preset power range is less than the power of the first noise signal when the active noise reduction system generates howling;

[0102] (4) the difference between the total power and the filtered power is greater than or equal to a second difference threshold; the filtered power is the result of filtering out the maximum power of the noise signal from the total power;

[0103] (5) there is a signal segment in the first noise signal whose power difference with the adjacent signal segment is greater than or equal to a third difference threshold.

[0104] The adjacent signal segment of the signal segment i is the signal segment i-1 and the signal segment i+1. Understandably, the adjacent signal segment of the first signal segment 1 is the adjacent signal segment 2, and the adjacent signal segment of the last signal segment P is the signal segment P-1. Wherein, 1≤i≤P.

[0105] If none of the above conditions is met, it is determined that the active noise reduction system does not generate stable single-frequency abnormal sound.

[0106] It should be noted that any one of the ratio threshold, the preset power range, the first difference threshold, the second difference threshold and the third difference threshold can be determined according to experimental data, or can be dynamically optimized.

[0107] In one embodiment, the feature includes a sound pressure value, and the abnormal sound detection is performed according to the sound pressure value; when the sound pressure value meets the abnormal sound condition, it is determined that the active noise reduction system has stable single-frequency abnormal sound.

[0108] The power E of the nth signal segment is calculated according to the amplitude of each signal segment by the root mean square effective value RMS n , and the calculation formula is Where N is the total number of sampling points of each signal segment, is the amplitude of the jth sampling point in the time domain, and the total number of sampling points is related to the sampling time and the sampling frequency. Then the sound pressure value is calculated according to the power of each signal segment, wherein the sound pressure value represents the intensity of the pressure change caused by the sound wave in the air or other medium, and the sound pressure value is expressed in decibels (dB) as a unit, and the calculation formula is dB=20lg(E nE0 is a standard sound pressure value, which can be set according to actual situations. Since the active noise reduction does not have a stable single-frequency abnormal sound, or the noise signal in the unstable state is a random signal, the power distribution is averaged in each signal segment, and the sound pressure value of a certain frequency segment is not particularly high. According to the sound pressure value of each signal segment, it can be judged whether the active noise reduction system has a stable single-frequency abnormal sound.

[0109] In one embodiment, the abnormal sound condition is P i -P i-1 ≥ T1, and P i -P i+1 ≥ T1, where T1 is a first threshold value, P i is a sound pressure value of the i-th signal segment, P i-1 and P i+1 are sound pressure values of adjacent signal segments of P i . The steps of abnormal sound detection according to the sound pressure value include: in response to P i -P i-1 ≥ T1, and P i -P i+1 ≥ T1, determining that the active noise reduction system has a stable single-frequency abnormal sound.

[0110] Figure 6 A method flowchart for calculating the sound pressure value of each signal segment is provided in an exemplary embodiment of the present disclosure, wherein the sound pressure value of each signal segment is calculated, including:

[0111] The amplitude of each signal segment is calculated by the root mean square effective value to obtain the power in each signal segment, and the sound pressure value of each signal segment is calculated by A weighting.

[0112] In this embodiment, the sampling time of the first noise signal is 5s, the sampling frequency is 6400Hz, the number of sampling points per second is 6400, and the total number of samples is 5*6400=32000. After data integration of the power of each frequency band, 1 / 1 octave diagram or 1 / 3 octave diagram is obtained, and then A weighting calculation and dB conversion are performed to obtain the sound pressure value of each signal segment.

[0113] Because the human ear is not sensitive to all frequencies, even if the sound pressure level is the same, but if the frequency is different, the human ear hears different feelings, so the sound pressure level actually heard needs to be corrected by a gain factor, which is called acoustic weighting. There are four kinds of A, B, C and D weighting methods in acoustic weighting, and the result of A weighting is the closest to human feeling. If 1 / 3 octave is used to represent the result, the result of the weighting is that the linear unweighted value of each octave band is added or subtracted by the corresponding dB value in the upper and lower diagrams, so from the perspective of frequency weighting, it is equivalent to algebraic calculation.

[0114] Frequency / Hz A-weighted Frequency / Hz A-weighted 10 -70.4 500 -3.2 12.5 -63.4 630 -1.9 16 -56.7 800 -0.8 20 -50.5 1000 0 25 -44.7 1250 0.6 31.5 -39.4 1600 1 40 -34.6 2000 1.2 50 -30.2 2500 1.3 63 -26.2 3150 1.2 80 -22.5 4000 1 100 -19.1 5000 0.5 125 -16.1 6300 -0.1 160 -13.4 8000 -1.1 200 -10.9 10000 -2.5 250 -8.6 12500 -4.3 315 -6.6 16000 -6.6 400 -4.8 20000 -9.3

[0115] In the embodiment, the power in each signal segment is calculated by the root mean square effective value, and then the A-weighting calculation is performed, so that the evaluation of the human ear to the abnormal sound can be better reflected, and the misjudgment of the abnormal sound in the low frequency band is reduced.

[0116] In one embodiment, if the amplitudes of two adjacent signal segments are both high, the two adjacent signal segments are combined into one signal segment for abnormal sound detection.

[0117] Since the noise signal in the no-abnormal-sound state is a random signal, the power distribution in each signal segment is average, and there is no phenomenon that the power in a certain frequency band is particularly high. When the sound pressure value P i of the i-th signal segment is greater than the sound pressure values P i-1 and P i+1 of the two adjacent signal segments, and all exceed the first threshold T1, it is represented that the sound pressure value of the signal segment at this time is much higher than that of the adjacent signal segment, and the stable single-frequency abnormal sound occurs, so that it is determined that the active noise reduction system has the stable single-frequency abnormal sound.

[0118] In the embodiment, by comparing the sound pressure values of the adjacent signal segments, the signal segment in which the howling and the stable single-frequency abnormal sound occur can be effectively and accurately detected, so that it is determined that the active noise reduction system has the stable single-frequency abnormal sound.

[0119] In one embodiment, the first threshold T1 is obtained according to the existing abnormal sound statistical data, and the first threshold T1 is 6-8db, preferably 7db.

[0120] In one embodiment, the first threshold is obtained by dynamic optimization to improve the accuracy of abnormal sound detection. The following describes an implementable way of dynamically selecting the first threshold, and the first threshold is determined by the following way:

[0121] S1, obtain abnormal signal samples and normal samples.

[0122] The abnormal samples are noise signals containing abnormal sounds, and the normal samples are noise signals not containing abnormal sounds. The abnormal samples can be collected by experiments, or can be the result of superimposing noise signals with a frequency and amplitude matched with the abnormal sound on the normal samples.

[0123] The number of abnormal samples and normal samples can be set according to actual needs. Understandably, the more the number of samples, the better the difference threshold optimization can be performed.

[0124] S2, construct a target function.

[0125] The target function is constructed according to at least one of the following parameters: the number of missed abnormal samples, the number of misjudged normal samples, the proportion of missed abnormal samples, and the proportion of misjudged normal samples.

[0126] The design principle of the objective function is to find a combination of decision parameters and values such that the first threshold obtained through optimization can detect all abnormal sounds, while normal signals are not misjudged as abnormal sounds, and at the same time, it has good robustness. The objective function for parameter optimization can be set as follows: (1) (the number of missed detections of abnormal samples + the number of false detections of normal samples) / the total number of samples, and in this case, the smaller the objective function, the better; (2) (the abnormal sound determination intensity of normal samples) / (the abnormal sound determination intensity of abnormal signals), and in this case, the smaller the objective function, the better.

[0127] In addition to the above objective function, other calculation formulas similar to those that conform to the design principle of the objective function can also be used, and the embodiments of the present disclosure do not make specific limitations.

[0128] S3. Optimize the objective function within the preset optimization range of the first threshold to determine the final difference threshold.

[0129] In one embodiment, the preset optimization range is [1dB, 10dB], that is, the first threshold searches for the optimal solution within [1dB, 10dB] to make the objective function the minimum value.

[0130] In this embodiment, providing the optimization range of the first threshold enables the optimization to conform to the physical characteristics of certain abnormal sounds, which can improve the optimization accuracy and efficiency.

[0131] In one embodiment, the optimization algorithm includes at least one of the following algorithms: genetic algorithm, simulated annealing algorithm, particle swarm algorithm, Bayesian optimization algorithm, and hill climbing algorithm.

[0132] In one embodiment, the abnormal sound condition is P i -P i-1 ≥T1, P i -P i+1 <T1, and P i+1 -P i+2 ≥T1, where P i-1 and P i+1 are the sound pressure values of adjacent signal segments of P i and P i and P i+2 are the sound pressure values of adjacent signal segments of P i+1 The steps for abnormal sound detection based on the sound pressure value include: in response to P i -P i-1 ≥T1, P i -P i+1 <T1, and P i+1 -P i+2 ≥T1, determine that there is a stable single-frequency abnormal sound in the active noise reduction system.

[0133] Generally, the abnormal sound frequency range is narrow and only appears in one frequency band. In a few cases, the abnormal sound frequency range is wide and the amplitudes of the adjacent frequency bands are both high. When the amplitudes of the adjacent frequency bands are both high, the two frequency bands are regarded as a part, i.e., P i and P i+1 are regarded as a part, and it is determined whether P i is higher than P i-1 and P i+1 is higher than P i+2 exceeds the first threshold T1.

[0134] Figure 7 Another noise signal frequency band diagram based on one-third octave provided for an exemplary embodiment of the present disclosure is shown in the box in the figure. If P i-1 and P i+1 are both high, for example, the black dashed box in the figure represents normal sound, at this time, three frequency bands are involved, and the frequency span range is large, and the three frequency bands cannot be combined into one frequency band for comparison with adjacent frequency bands. At this time, it is a normal sound, and this state is not determined as a stable single-frequency abnormal sound.

[0135] In the present embodiment, when the abnormal sound frequency range is wide, resulting in the amplitudes of the adjacent frequency bands being both high, by judging the sound pressure values of other frequency bands adjacent to the two frequency bands, it is determined whether the two adjacent frequency bands have a stable single-frequency abnormal sound, so that the howling and stable single-frequency abnormal sound judgment is more accurate, and the misjudgment of the abnormal sound is also avoided.

[0136] In one embodiment, the abnormal sound condition is P-P i <T2, where T2 is a second threshold, and P is the sum of the sound pressure values of the signal segments. The steps of abnormal sound detection based on the sound pressure values include: in response to P-P i <T2, it is determined that the active noise reduction system has a stable single-frequency abnormal sound.

[0137] First, the sum of the sound pressure values of the signal segments is calculated as the total sound pressure value P, and the difference between the sound pressure value of a signal segment and the total sound pressure value can be used to evaluate the power proportion of the signal segment. The smaller the difference, the greater the proportion, indicating that the power of the frequency band of the signal segment accounts for a large proportion of the total power of the sound, and the power is mainly concentrated in this signal segment. When a stable single-frequency abnormal sound occurs, the frequency band power of the corresponding signal segment is relatively large, i.e., the difference between the sound pressure value of the signal segment and the total sound pressure value is small, and abnormal sound detection is performed through this feature. When the difference between the sound pressure value of a signal segment and the total sound pressure value is less than the second threshold T2, it is determined that the active noise reduction system has a stable single-frequency abnormal sound.

[0138] In the embodiment, the proportion of the power of each signal segment in the entire noise signal is determined by comparing each signal segment with the total sound pressure value, so that the abnormal sound can be quickly detected without multiple comparisons of the sound pressure value, and the calculation power is reduced.

[0139] In one embodiment, the second threshold T2 is obtained according to existing abnormal sound statistical data, and T2 is in the range of 2-4db, preferably 3db.

[0140] In one embodiment, the second threshold is obtained by dynamic optimization to improve the accuracy of abnormal sound detection. The dynamic optimization process of the second threshold is similar to that of the first threshold, which will not be described here.

[0141] In one embodiment, the above abnormal sound conditions can be used in combination, for example, the abnormal sound conditions include: (1) P i -P i-1 ≥T1, and P i -P i+1 ≥T1; (2) P i -P i-1 ≥T1, P i -P i+1 <T1, and P i+1 -P i+2 ≥T1; (3) P-P i <T2. When one of the conditions is met, it is determined that a stable single-frequency abnormal sound occurs.

[0142] If a signal segment meets any one of the conditions, it means that the power of the signal segment accounts for a large proportion of the total power of the first noise signal. Only stable single-frequency abnormal sound can occur in this case. The reason why normal noise signals cannot occur in 1 / 3 octave band is that normal noise signals are random signals, and the power is evenly distributed in all frequency bands, so it will not appear that the power of a certain frequency band is particularly high.

[0143] In the embodiment, the active noise reduction system can be determined whether to occur stable single-frequency abnormal sound by combining multiple abnormal sound conditions, so that the determination is more accurate.

[0144] The device equipped with the active noise reduction system, such as an extractor hood, may also produce sound with spectral characteristics similar to the stable single-frequency sound caused by the active noise reduction system, which we call "normal abnormal sound". Although this "normal abnormal sound" also makes users feel uncomfortable, it is different from the stable single-frequency sound caused by the active noise reduction system, and is not a "byproduct" of the active noise reduction system. It cannot be reduced by turning off or adjusting the active noise reduction system.

[0145] Based on this, in one embodiment, if the detection result of step 103 is that there is a stable single-frequency abnormal sound, in order to exclude that the abnormal sound is caused by the device equipped with the active noise reduction system rather than the active noise reduction system, first, the active noise reduction system is closed, the second noise signal in the process of closing the active noise reduction system is obtained, and whether the second noise signal still has a stable single-frequency abnormal sound is judged based on steps 102-104; if the judgment result is that the second noise signal has a stable single-frequency abnormal sound, and the abnormal sound frequency of the second noise signal is the same as or similar to that of the first noise signal, it is determined that the abnormal sound is caused by the device rather than the active noise reduction system; otherwise, it is determined that the active noise reduction system has a stable single-frequency abnormal sound.

[0146] Another implementation mode of excluding the interference of the device abnormal sound on the active noise reduction system abnormal sound detection is provided below.

[0147] In one embodiment, step 101 further includes:

[0148] Step 100-1: obtaining a second noise signal of the noise source in the case where the active noise reduction system is not started.

[0149] In one embodiment, since the frequency band range where the abnormal sound often occurs is 20-3000 Hz, step 100-1 can collect the second noise signal with the frequency band of 20-3000 Hz. Therefore, for signals not in this frequency band range, there is no need to calculate, which can reduce the calculation amount and improve the rate of abnormal sound detection.

[0150] Step 100-2: performing octave calculation on the second noise signal to determine a second segmentation strategy matched with the second noise signal.

[0151] 1 / n octaves, n is a non-zero natural number such as 1, 2, 3, etc., which takes into account detection accuracy and computing power, and preferably n=3.

[0152] Step 100-3: segmenting the second noise signal according to the second segmentation strategy and calculating the characteristic value of each signal segment of the second noise signal.

[0153] The characteristic value includes power and / or decibel value.

[0154] Step 100-4: determining the target gain of the first target signal segment with the characteristic value greater than the characteristic value threshold.

[0155] The characteristic value of the first target signal segment after filtering the first target signal segment with the bandpass filter of the target gain is less than or equal to the characteristic value threshold.

[0156] The characteristic value of the first target signal segment being greater than the characteristic value threshold indicates that the first target signal segment contains abnormal sound, and the active noise reduction system was not started when the first target signal segment was collected, meaning that the abnormal sound contained in the first target signal segment is caused by the device carrying the active noise reduction system, rather than by the active noise reduction system. In order to exclude device abnormal sound interference during abnormal sound detection, the second target signal segment contained in the first noise signal needs to be filtered to filter the abnormal sound part caused by the device.

[0157] Taking the characteristic value as a decibel value as an example, it is assumed that the decibel value of the first target signal segment before filtering is P, and the decibel value after filtering by the bandpass filter with the target gain is P' (less than or equal to the characteristic value threshold). Here, P' is set to be greater than the decibel value of the adjacent frequency band by 1 dB. According to the formula: P' = 20lg(E' / E0), the power E' of the first target signal segment after filtering can be calculated. By knowing the power E of the first target signal segment before filtering and the power E' after filtering, the target gain / through rate of the first target signal segment is calculated as α = E' / E.

[0158] It should be noted that P' is set to be greater than the decibel value of the adjacent frequency band by 1 dB, rather than being equal, in order to make abnormal sound monitoring more sensitive. The former is more sensitive to abnormal sound judgment than the latter. When the judgment condition is 3 dB higher than the adjacent frequency band, it requires less power to judge abnormal sound than when it is equal to the adjacent frequency band. At this time, the abnormal sound judgment sensitivity is higher while the accuracy is guaranteed.

[0159] In step 103, the center frequency, upper and lower limit frequencies of the bandpass filter are designed according to the first segmentation strategy, and the gain / through rate α of the bandpass filter of the second target signal segment is designed according to the target gain of the first target signal segment, so that the gain of the bandpass filter of the second target signal segment is the target gain. The frequency band of the second target signal segment contains the frequency band of the first target signal segment.

[0160] The bandpass filter with the target gain is used to filter the second target signal segment, that is, α (<1) is multiplied by the amplitude of the second target signal segment to filter the abnormal sound part caused by the device in the second target signal segment. The amplitudes of the signal segments other than the second target signal segment in the second noise signal are not attenuated, that is, the power through rate of the other signal segments is 100%.

[0161] In this embodiment, the bandpass filter with the target gain is used to filter the signal segments of the target signal segments in the second noise signal, which can exclude the interference of device abnormal sound on the abnormal sound detection of the active noise reduction system and further improve the accuracy of abnormal sound detection.

[0162] Figure 8A flow chart of a control method of an active noise reduction system is provided for an example embodiment of the present disclosure, the control method comprising: detecting abnormal noise of the active noise reduction system according to any one of the abnormal noise detection methods described above, and restarting the active noise reduction system in response to the active noise reduction system having stable single-frequency abnormal noise.

[0163] In this embodiment, it is determined whether the active noise reduction system has stable single-frequency abnormal noise by the abnormal noise detection method in the above-described embodiments, and if so, the active noise reduction system is restarted, and if not, the abnormal noise is continuously monitored in real time. The active noise reduction system can be restarted in time in response to abnormal noise, preventing subsequent abnormal noise from affecting the user and improving the user experience.

[0164] The present disclosure also provides an active noise reduction system, comprising: a loudspeaker, a microphone, and a processor; the processor is connected to the loudspeaker and the microphone, and the processor implements the method of any one of the above-described embodiments when executing a computer program.

[0165] Figure 9 A structural schematic diagram of an electronic device is shown for an example embodiment of the present disclosure, the electronic device comprising a memory, a processor, and a computer program stored in the memory and used to run on the processor, and the processor implements the method of any one of the above-described embodiments when executing the computer program. Figure 9 The electronic device 90 shown is merely an example and should not impose any limitation on the functions and use range of the embodiments of the present disclosure.

[0166] As shown in Figure 9 The electronic device 90 can be in the form of a general computing device, for example, it can be a server device. The components of the electronic device 90 can include but are not limited to: the above-described at least one processor 91, the above-described at least one memory 92, and a bus 93 connecting different system components including the memory 92 and the processor 91.

[0167] The bus 93 includes a data bus, an address bus, and a control bus.

[0168] The memory 92 can include volatile memory, such as a random access memory (RAM) 921 and / or a cache memory 922, and can further include a read-only memory (ROM) 923.

[0169] The memory 92 can further include a program tool 925 (or utility tool) having a set of (at least one) program modules 924, such as an operating system, one or more application programs, other program modules, and program data, and each of these examples or some combination thereof can include the implementation of a network environment.

[0170] The processor 91 performs the various function applications and data processing by running the computer programs stored in the memory 92, such as the methods provided in any of the above embodiments.

[0171] The electronic device 90 can also communicate with one or more external devices 94 such as a keyboard or a pointing device, by the I / O interface 95. Furthermore, the electronic device 90 can communicate with one or more networks, such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet, by the network adapter 96. As depicted, the network adapter 96 communicates with the other modules of the electronic device 90 through the bus 93. It should be appreciated that although not shown, other hardware and / or software modules could be used in connection with the electronic device 90. These include, but are not limited to, microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.

[0172] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the foregoing detailed description, such division is merely exemplary and not mandatory. Indeed, according to an embodiment of the present disclosure, features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, features and functions of one unit / module described above can be further divided into multiple units / modules.

[0173] The present disclosure also provides a computer readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided in any of the above embodiments.

[0174] More specifically, the computer readable storage medium can include, but is not limited to, a portable disc, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0175] The present disclosure also provides a computer program product comprising a computer program, which, when executed by a processor, implements the method described in any of the above embodiments.

[0176] The program code for carrying out the computer program product of the present disclosure can be written in any combination of one or more programming languages, and can be executed entirely on the user device, partly on the user device and partly on a remote device, as a stand-alone software package, partly on the user device and partly on a remote device, or entirely on a remote device.

[0177] Although the specific embodiments of the present disclosure are described above, those skilled in the art should understand that this is only an example, and the protection scope of the present disclosure is defined by the appended claims. Those skilled in the art can make various changes or modifications to the embodiments without departing from the principles and essence of the present disclosure, and these changes and modifications all fall within the protection scope of the present disclosure.

Claims

1. A method for detecting abnormal sounds, characterized in that, The method is applied to an active noise cancellation system, which includes a speaker and a microphone; the abnormal sound detection method includes: A first noise signal is acquired during the operation of the active noise cancellation system; the first noise signal includes the noise signal of the noise source acquired by the microphone and / or the control signal of the speaker; The first noise signal is subjected to octave band calculation to determine a first segmentation strategy that matches the first noise signal; A bandpass filter is designed based on the first segmentation strategy, and the first noise signal is filtered using the bandpass filter to obtain at least three signal segments. Based on the characteristics of the at least three signal segments, the active noise reduction system is subjected to stable single-frequency abnormal sound detection. The characteristics of stable single-frequency abnormal sound are: there is a prominent peak in the noise spectrum, the amplitude of the highest point of the peak is higher than the amplitude of the adjacent frequency, and the power of the peak accounts for less than or equal to the power threshold of the total noise power.

2. The abnormal sound detection method according to claim 1, characterized in that, Perform octave band calculations on the first noise signal to determine a first segmentation strategy that matches the first noise signal, including: Perform octave band calculation on the first noise signal to determine the center frequency of the octave band of the first noise signal; The upper and lower frequency limits included in the first segmentation strategy are determined based on the center frequency.

3. The abnormal sound detection method according to claim 2, characterized in that, The step of determining the upper and lower frequency limits included in the first segmentation strategy based on the center frequency includes: When calculating the first noise signal using 1 / 1 octave bands, the upper limit frequency and the lower limit frequency are calculated using the following formulas: The center frequency of the first octave band of the noise signal is denoted as f. c1 The upper limit frequency is expressed as f. high1 The lower limit frequency is denoted as f. low1 ; Alternatively, when calculating the first noise signal using 1 / 3 octave bands, the upper limit frequency and the lower limit frequency can be calculated using the following formula: f high2 =f c2 ×2 1 / 6 ,f low2 =f c2 / 2 1 / 6 ; The center frequency of the first noise signal at 1 / 3 octave band is denoted as f. c2 The upper limit frequency is expressed as f. high2 The lower limit frequency is denoted as f. low2 .

4. The abnormal sound detection method according to claim 1, characterized in that, Based on the characteristics of the at least three signal segments, the active noise cancellation system performs abnormal sound detection, including: In response to the feature meeting the abnormal sound judgment condition, it is determined that the active noise cancellation system has a stable single-frequency abnormal sound; The criteria for determining abnormal sounds include at least one of the following: The first noise signal contains signal segments with a power percentage greater than or equal to a percentage threshold; the feature includes a power percentage; the power percentage is the ratio of the power of a signal segment to the total power, and the total power is the sum of the power of each signal segment; The difference between the total power and the maximum power of the signal segment is less than a first difference threshold; the feature includes the power of the signal segment; The total power is within a preset power range; wherein, the lower limit of the preset power range is greater than the maximum power of the noise signal of the noise source, and the upper limit of the preset power range is less than the power of the first noise signal when the active noise cancellation system generates a howling sound; The difference between the total power and the filtered power is greater than or equal to a second difference threshold; the filtered power is the result of filtering out the maximum power of the noise signal from the total power; The first noise signal contains a signal segment whose power difference with the adjacent signal segment is greater than or equal to the third difference threshold.

5. The abnormal sound detection method according to claim 1, characterized in that, The feature includes sound pressure level; Based on the characteristics of the at least three signal segments, the active noise cancellation system performs abnormal sound detection, including: When the sound pressure value meets the abnormal sound condition, it is determined that the active noise cancellation system has a stable single-frequency abnormal sound; The abnormal sound conditions include at least one of the following: P i -P i-1 ≥T1, and P i -P i+1 ≥T1; where T1 is the first threshold, P i Let P be the sound pressure level of the i-th signal segment. i-1 and P i+1 For P i The sound pressure level of adjacent signal segments; P i -P i-1 ≥T1, P i -P i+1 <T1, and P i+1 -P i+2 ≥T1, where P i+2 is the sound pressure value of the adjacent signal segment of P i+1 ; P-P i <T2, where T2 is a second threshold value and P is the sum of the sound pressure values of each signal segment.

6. The abnormal sound detection method according to any one of claims 1-5, characterized in that, Before the step of acquiring the first noise signal during the operation of the active noise cancellation system, the following steps are also included: When the active noise cancellation system is not activated, a second noise signal is acquired; the second noise signal is the noise signal of the noise source collected by the microphone. The second noise signal is subjected to octave band calculation to determine a second segmentation strategy that matches the second noise signal; The second noise signal is segmented according to the second segmentation strategy, and the characteristic values ​​of each signal segment of the second noise signal are calculated. Determine the target gain of a first target signal segment whose eigenvalue is greater than a eigenvalue threshold; after filtering the first target signal segment with a bandpass filter of the target gain, the eigenvalue of the first target signal segment is less than or equal to the eigenvalue threshold; The design of a bandpass filter based on the first segmentation strategy includes: designing the frequency band of the bandpass filter based on the center frequency, upper limit frequency, and lower limit frequency included in the first segmentation strategy; and designing the gain of the bandpass filter for the second target signal segment based on the target gain; wherein the frequency band of the second target signal segment includes the frequency band of the first target signal segment.

7. A control method for an active noise reduction system, characterized in that, include: The abnormal noise detection method according to any one of claims 1 to 6 is used to detect abnormal noise in the active noise cancellation system; In response to the presence of a stable single-frequency abnormal sound in the active noise cancellation system, the active noise cancellation system is restarted.

8. An active noise cancellation system, characterized in that, The active noise cancellation system includes: a speaker, a microphone, and a processor; the processor is connected to the speaker and the microphone respectively, and when the processor executes a computer program, it implements the abnormal sound detection method as described in any one of claims 1-6 or the control method of the active noise cancellation system as described in claim 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the abnormal sound detection method according to any one of claims 1 to 6 or the control method of the active noise reduction system according to claim 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the abnormal sound detection method as described in any one of claims 1 to 6 or the control method of the active noise reduction system as described in claim 7.

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