Active noise reduction system, control method thereof, and abnormal sound detection method and device

Abstract

The disclosure provides an active noise reduction system and a control method thereof, an abnormal sound detection method and device. The abnormal sound detection method comprises: acquiring a first noise signal in the operation process of the active noise reduction system; the first noise signal comprises a noise signal of a noise source collected by a microphone and / or a control signal of the loudspeaker; performing Fourier transform on the first noise signal, and determining the total spectral power amplitude and the noise peak value of the first noise signal according to the Fourier transform result; in response to the difference between the total spectral power amplitude and the filtering power being greater than or equal to a difference threshold value, it is determined that the active noise reduction system has stable single-frequency abnormal sound. The disclosure determines the total spectral power amplitude and the noise peak value related to the abnormal sound in the first noise signal by performing spectral analysis on the first noise signal, and then accurately identifies the abnormal sound according to the total spectral power amplitude and the noise peak value, thereby improving the abnormal sound detection accuracy of the active noise reduction system.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of active noise reduction technology, and in particular to an active noise reduction system, a control method thereof, and a method and device for detecting abnormal sound. BACKGROUND

[0002] In an 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, thereby achieving the purpose of noise reduction. However, in this process, when the feedback signal is greater than the noise signal, a howling phenomenon occurs. 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 value, 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 in the amplitude of the loudspeaker control signal generated when the system is working normally and the control signal generated when the stable single-frequency abnormal sound exists. Therefore, through the traditional howling monitoring scheme based on the amplitude of the loudspeaker control signal in the prior art, the stable single-frequency abnormal sound phenomenon cannot be effectively identified. SUMMARY

[0003] The technical problem to be solved by the present disclosure is to overcome the above-mentioned defects in the prior art, and to provide an active noise reduction system, a control method thereof, and a method and device for detecting abnormal sound.

[0004] The present disclosure solves the above technical problems by the following technical solutions:

[0005] In a first aspect, a method for detecting abnormal sound is provided, which is applied to an active noise reduction system including a microphone and a loudspeaker. The method includes:

[0006] obtaining a first noise signal in the running process of the active noise reduction system; the first noise signal includes a noise signal of a noise source collected by the microphone and / or a control signal of the loudspeaker;

[0007] performing Fourier transform on the first noise signal, and determining a total spectral power amplitude and a noise peak value of the first noise signal according to the Fourier transform result;

[0008] detecting abnormal sound of the active noise reduction system according to the total spectral power amplitude and the noise peak value.

[0009] Optionally, detecting abnormal sound of the active noise reduction system according to the total spectral power amplitude and the noise peak value includes:

[0010] in response to a difference between the total spectral power amplitude and the noise peak value being less than a difference threshold value, determining that the active noise reduction system has stable single-frequency abnormal sound;

[0011] The difference threshold is determined by the following manner:

[0012] Obtaining an abnormal sample and a normal sample; the abnormal sample is a noise signal containing abnormal sound, and the normal sample is a noise signal not containing abnormal sound;

[0013] Constructing a target function; the target function is constructed according to at least one of the following parameters: the number of missed detections of abnormal samples, the number of false detections of normal samples, the proportion of missed detections of abnormal samples, and the proportion of false detections of normal samples;

[0014] Optimizing the target function in a preset optimization range of the difference threshold to determine the final difference threshold.

[0015] Optionally, the preset optimization range is [1dB, 10dB];

[0016] And / or, the optimization algorithm includes at least one of the following algorithms: genetic algorithm, simulated annealing algorithm, particle swarm algorithm, Bayesian optimization algorithm, hill climbing algorithm;

[0017] And / or, the target function is the ratio of the number of abnormalities to the total number of samples, and the number of abnormalities is the sum of the number of missed detections of abnormal samples and the number of false detections of normal samples.

[0018] Optionally, if the frequency spectrum of the first noise signal contains at least two peak values, the largest peak value is determined as the noise peak value.

[0019] Optionally, determining the total amplitude of the frequency spectrum power and the noise peak value of the first noise signal according to the Fourier transform result includes: performing A-weighting processing on the Fourier transform result, and determining the total amplitude of the frequency spectrum power and the noise peak value of the first noise signal according to the A-weighting processing result.

[0020] And / or, the Fourier transform result is divided into at least two noise segments, and the total amplitude of the frequency spectrum power and the noise peak value are determined according to the integral of each noise segment;

[0021] And / or, in response to the difference between the total amplitude of the frequency spectrum power and the noise peak value being less than the difference threshold, determining that the active noise reduction system has a stable single-frequency abnormal sound, including: in response to the number of times that the difference between the total amplitude of the frequency spectrum power and the noise peak value is less than the difference threshold is greater than a threshold number, determining that the active noise reduction system has a stable single-frequency abnormal sound.

[0022] Optionally, before obtaining the first noise signal in the running process of the active noise reduction system, the method further includes:

[0023] Obtaining a second noise signal of a noise source when the active noise reduction system is not started;

[0024] segmenting the second noise signal according to the segment strategy, and calculating a feature value of each noise segment of the second noise signal; the feature value comprises a power and / or a decibel value;

[0025] performing Fourier transform on the first noise signal, comprising:

[0026] segmenting the first noise signal according to the segment strategy, and performing Fourier transform on the noise segment obtained by the segmentation.

[0027] Optionally, before obtaining the first noise signal in the operation process of the active noise reduction system, the method further comprises:

[0028] obtaining a second noise signal of a noise source in a case where the active noise reduction system is not started;

[0029] performing octave calculation on the second noise signal to determine a segment strategy matched with the second noise signal;

[0030] segmenting the second noise signal according to the segment strategy, and calculating a feature value of each noise segment of the second noise signal; the feature value comprises a power and / or a decibel value;

[0031] determining a target gain of a first target noise segment with a feature value greater than a feature value threshold; and filtering the first target noise segment with a band-pass filter of the target gain, so that the feature value of the first target noise segment after the filtering is less than or equal to the feature value threshold;

[0032] performing Fourier transform on the first noise signal, comprising:

[0033] segmenting the first noise signal to obtain at least two noise segments, filtering a second target noise segment contained in the first noise signal with a band-pass filter of a target gain, and performing Fourier transform on the noise segments obtained by the segmentation and the filtered noise segment in the first noise signal respectively; wherein a frequency band of the second target noise segment contains a frequency band of the first target noise segment.

[0034] In a second aspect, a control method of an active noise reduction system is provided, comprising:

[0035] detecting an abnormal sound of the active noise reduction system according to the abnormal sound detection method of any one of the first aspect;

[0036] in response to the active noise reduction system having a stable single-frequency abnormal sound, restarting the active noise reduction system.

[0037] In a third aspect, an abnormal sound detection device is provided, which is applied to an active noise reduction system, and the active noise reduction system comprises a microphone and a speaker; the abnormal sound detection device is used to implement the abnormal sound detection method in any one of the first aspect; the abnormal sound detection method comprises:

[0038] an acquisition module, configured to acquire a first noise signal in the running process of the active noise reduction system; the first noise signal is a noise signal of a noise source collected by the microphone or a control signal of the speaker;

[0039] a determination module, configured to perform Fourier transform on the first noise signal, and determine a total spectral power amplitude and a noise peak value of the first noise signal according to the Fourier transform result;

[0040] a detection module, configured to determine that the active noise reduction system has a stable single-frequency abnormal sound in response to a difference between the total spectral power amplitude and the noise peak value being less than a difference threshold.

[0041] In a fourth aspect, an active noise reduction system is provided, which comprises 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 in any one of the first aspect or the second aspect when executing the computer program.

[0042] On the basis of common sense in the art, the above-mentioned preferred conditions can be combined arbitrarily, that is, to obtain each preferred example of the present disclosure.

[0043] The positive progress effect of the present disclosure is that: the present disclosure determines the total spectral power amplitude and the noise peak value related to the abnormal sound in the first noise signal through spectral analysis on the first noise signal, and then accurately identifies the abnormal sound according to the total spectral power amplitude and the noise peak value, thereby improving the abnormal sound detection accuracy of the active noise reduction system. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 A flowchart of an abnormal sound detection method provided for an exemplary embodiment of the present disclosure is provided;

[0045] Figure 2 A frequency domain curve schematic diagram of a stable single-frequency abnormal sound, a howling, and normal noise provided for an exemplary embodiment of the present disclosure is provided;

[0046] Figure 3 A time domain power curve schematic diagram of a stable single-frequency abnormal sound, a howling, and normal noise provided for an exemplary embodiment of the present disclosure is provided;

[0047] Figure 4 A flowchart of difference threshold optimization in an abnormal sound detection method provided for an exemplary embodiment of the present disclosure is provided;

[0048] Figure 5A curve effect comparison diagram of a method for detecting abnormal sound provided by an example embodiment of the present disclosure, with or without A-weighting processing;

[0049] Figure 6 A flowchart of another method for detecting abnormal sound provided by an example embodiment of the present disclosure;

[0050] Figure 7 A flowchart of another method for detecting abnormal sound provided by an example embodiment of the present disclosure;

[0051] Figure 8 A module diagram of an abnormal sound detection device provided by an example embodiment of the present disclosure;

[0052] Figure 9 A module diagram of an active noise reduction system provided by an example embodiment of the present disclosure. DETAILED DESCRIPTION

[0053] The present disclosure will be further described by way of examples, but the present disclosure is not limited to the examples described.

[0054] In the embodiments of the present disclosure, the prefix words such as "first", "second" 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 be referred to the description of the context in the claims or embodiments, and should not constitute an unnecessary 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.

[0055] Figure 1 A flowchart of a method for detecting abnormal sound provided by an example embodiment of the present disclosure, which is applied to an active noise reduction system including devices such as a nearby microphone, a speaker and a controller.

[0056] The abnormal sound detected in the embodiments of the present disclosure refers to an abnormal sound emitted by the speaker of the active noise reduction system due to some faults or interference when the active noise reduction system is working / running, which will affect the user experience. The abnormal sound includes howling and stable single-frequency abnormal sound (or stable single-frequency sound).

[0057] The characteristics of howling of the active noise reduction are as follows: see Figure 2 and Figure 3The 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 2 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.

[0058] 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 2 As shown by the blue curve, the bandwidth of this peak does not exceed 50Hz. See also Figure 3 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 3 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.

[0059] This embodiment of the disclosure primarily detects whether a stable single-frequency abnormal sound occurs in the active noise cancellation system.

[0060] See Figure 1 The abnormal sound detection method includes the following steps:

[0061] Step 101: Obtain the first noise signal during the operation of the active noise cancellation system.

[0062] The first noise signal can be the noise signal emitted by the noise source collected by the first microphone. This noise signal will be used as the basis for abnormal sound detection. The first microphone is deployed near the noise source. It should be noted that the first noise signal can be the result of a single first microphone or a combination of results from multiple first microphones. In this case, the first noise signal collected by the first microphone is the superposition of the original noise signal emitted by the noise source and the feedback signal.

[0063] The active noise reduction system can be used for noise reduction of electric appliances such as range hood, washing machine, etc. Taking noise reduction of the range hood as an example, the noise source may be, for example, the fan of the range hood, and the first microphone for collecting noise signals is arranged near the fan.

[0064] The first noise signal can be a control signal of the loudspeaker, which will be used as the basis data for detecting abnormal sound. It should be noted that the control signal of the loudspeaker can be obtained directly by obtaining 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 a control signal of one loudspeaker, or a combination of control signals of multiple loudspeakers.

[0065] The first noise signal can also be a combination of the noise signal of the noise source and the control signal of the loudspeaker, and the specific combination manner is not particularly limited in the embodiments of the present disclosure.

[0066] In one embodiment, the first noise signal meets the sampling frequency requirement, total sampling time requirement, etc. of Fourier transform processing, for example, the sampling frequency f s ≥ 2f U , where f U is the highest analysis frequency (for example, 12800 Hz) of the active noise reduction system in the noise signal collection process, and the highest analysis frequency is greater than or equal to the highest frequency f ANC at which abnormal sound may occur. For example, the frequency at which abnormal sound may occur is 700 Hz, 800 Hz and 900 Hz, f ANC is 900 Hz, and f U ≥ 900 Hz. The total sampling time T ≥ the Fourier transform duration T FFT . T FFT is determined by the frequency resolution Δf required by abnormal sound detection, and Δf ≤ 100 Hz. Preferably, Δf ≤ 20 Hz.

[0067] wherein the highest analysis frequency of the active noise reduction refers to the target control frequency of the active noise reduction, and the active noise reduction only analyzes noise in a certain frequency range and does not control noise that is not in the corresponding frequency range. The highest analysis frequency of the active noise reduction control is in the range of [600 Hz, 2000 Hz]. Preferably, the highest analysis frequency of the active noise reduction control is in the range of [600 Hz, 1000 Hz].

[0068] For example, in the scenario where the active noise reduction system is used for noise reduction of the range hood, the noise of the range hood is distributed in the range of 0-20000 Hz, and the frequency range of the analysis frequency of the active noise reduction may only be 0-1000 Hz. Therefore, the highest analysis frequency of the active noise reduction is set to 1000 Hz, which is sufficient because the abnormal sound caused by the active noise reduction system can only occur in the range of 0-1000 Hz.

[0069] In one embodiment, a first noise signal during the operation of the active noise cancellation system is acquired at a target sampling frequency. The target sampling frequency is determined based on the abnormal sound frequency and / or the computing power of the analysis device for the first noise signal; the abnormal sound frequency is determined based on noise samples containing abnormal sounds. The noise samples can be obtained experimentally or by superimposing abnormal sounds onto noise samples without abnormal sounds. The analysis device for the first noise signal is the device that analyzes the first noise signal to achieve abnormal sound detection, and may be, for example, an active noise cancellation chip.

[0070] The following describes one method for determining the target sampling frequency:

[0071] The method for determining the minimum target sampling frequency: By analyzing noise samples containing abnormal sounds, it can be seen that the maximum frequency (abnormal sound frequency) of the abnormal sound is generally f. ANC According to the Nyquist sampling theorem, to avoid aliasing, the target sampling frequency should be at least twice the frequency of the different audio frequencies. Therefore, the minimum value of the target sampling frequency f is... s_min =2f ANC .

[0072] The method for determining the maximum value of the sampling frequency: Analyzing the first noise signal includes performing a Fourier transform on it. The computing power of the analysis device can be characterized by the number of data points of the first noise signal in each Fourier transform. The maximum value of the target sampling frequency can be derived from the maximum computing power. Assuming the analysis device allows N data points to be transformed in one Fourier transform, the time of one Fourier transform is 1 / Δf, and the sampling frequency f... s Indicates f per second s Therefore, the number of data points allowed for a single Fourier transform is f. s ·1 / Δf, i.e., N=f s ·1 / Δf, assuming the maximum computing power of the analysis device is N and the maximum number of data points for each Fourier transform. max Then we can get f s_max =N max ·Δf.

[0073] Therefore, the target sampling frequency f of the first noise signal s_res The range of values ​​for is [f s_min f s_max In related technologies, to improve the control accuracy of active noise cancellation, the original signal acquired by the active noise cancellation controller has a high sampling frequency f. s , usually f s The target sampling frequency is 12000Hz. In this embodiment, the target sampling frequency f is less than 12000Hz. s_res Obtain the first noise signal.

[0074] In this embodiment, the first noise signal is acquired at the target sampling frequency determined according to the computing power of the analysis device of the abnormal sound frequency and / or the first noise signal, as the data basis for abnormal sound detection, which can provide data basis conforming to the characteristics of abnormal sound for the subsequent steps, eliminate interference, and further improve the accuracy of abnormal sound detection. On the other hand, the first noise signal acquired at the target sampling frequency eliminates the interference signal and reduces the data analysis amount, so as to improve the abnormal sound detection rate and meet the timeliness requirement.

[0075] In step 102, Fourier transform is performed on the first noise signal, and the total amplitude of the spectral power of the first noise signal and the noise peak value are determined according to the Fourier transform result.

[0076] The Fourier transform is performed on the first noise signal to obtain the spectral data (Fourier transform result) of the first noise signal. It is found through analysis of the spectral data that when the howling and stable single-frequency abnormal sound phenomenon occurs, there is a signal peak value obviously higher than the surrounding frequencies in the spectrum, as shown in the peak value near 95 Hz of the medium red dotted line and the peak value near 109 Hz of the blue thin solid line. It is found through the filtering playback that the noise corresponding to the peak value frequency is the sound that produces howling and stable single-frequency abnormal sound. The normal signal (medium green thick solid line) has no such obvious noise peak value, so the spectral analysis of the first noise signal can accurately identify the abnormal sound. Figure 3 Figure 3

[0077] In one embodiment, in order to improve the algorithm rate, the Fourier transform is performed on the first noise signal by using the fast Fourier transform (FFT).

[0078] In one embodiment, the number of signal data points processed by one FFT is selected to be close to the value of 2 FFT s_res N is a positive integer, and it is ensured that the selected N satisfies 2 N N≥T N / f FFT s_res The final spectral data D FFT The total power E FFT of D A is selected as the total amplitude of the spectral power. The maximum peak value E P in the spectrum is the maximum value of the entire FFT spectral analysis data.

[0079] In one embodiment, if the spectrum of the first noise signal contains at least two peak values, the maximum peak value is determined as the noise peak value.

[0080] ​​​​In one embodiment, the first noise signal is subjected to Fourier transform at a target frequency resolution. The target frequency resolution is obtained through optimization, with the optimization target being to accurately identify the abnormal sound and minimize the calculation amount of the abnormal sound detection.

[0081] The accurate identification of the abnormal sound refers to an identification accuracy greater than an accuracy threshold and / or a misidentification rate less than or equal to a misidentification threshold. The accuracy threshold and the misidentification threshold can be set according to actual conditions.

[0082] The frequency resolution determines the calculation amount of the abnormal sound detection process. When the frequency resolution is 1 Hz, it means that Fourier transform is performed every 1 s. Assuming that the calculation amount each time is the data amount within 1 s, K1, when the frequency resolution is increased to 4 Hz, it means that Fourier transform is performed every 1 / 4 s. At this time, the calculation amount each time is the data amount within 1 / 4 s, K2, K1 = 4 K2. The data amount when the frequency resolution is 4 Hz is 4 times less than that when the frequency resolution is 1 Hz, greatly accelerating the calculation amount speed of the abnormal sound detection. The following table shows the relevant parameters of Fourier transform at different frequency resolutions within 0-100 Hz for a test time of 4 s. It can be seen that the total number of times of calculation within 0-100 Hz within 4 s is the same, but the calculation amount of Fourier transform each time is different. It can be concluded from the table that the data amount of Fourier transform each time decreases as the frequency resolution increases.

[0083]

[0084] In addition, as the frequency resolution increases, the amplitude of the amplitude-frequency graph after Fourier transform increases, that is, when the frequency resolution increases, the number of spectral lines decreases, and the power of each spectral line is the sum of the power of the spectral lines before the frequency resolution increases.

[0085] In this embodiment, the first noise signal is subjected to spectrum analysis according to the target frequency resolution obtained through optimization. On the one hand, it can provide a data basis conforming to the characteristics of the abnormal sound, exclude interference, and thus improve the accuracy of the abnormal sound detection. On the other hand, the spectrum analysis of the first noise signal at the target frequency resolution minimizes the calculation amount of the abnormal sound detection, thereby improving the abnormal sound detection rate and meeting the timeliness requirement.

[0086] In one embodiment, the step of determining the target frequency resolution includes obtaining an abnormal sample, iteratively updating the frequency resolution, and determining the frequency resolution corresponding to the minimum calculation amount for detecting the abnormal sample as the target frequency resolution.

[0087] The abnormal sample is a noise signal containing an abnormal sound. The abnormal sample can be collected through experiments or be the result of superimposing a normal sample (a noise signal without an abnormal sound) on an abnormal sound.

[0088] In an embodiment, the optimization algorithm can include, but is not limited to, at least one of the following algorithms: genetic algorithm, simulated annealing algorithm, particle swarm algorithm, Bayesian optimization algorithm, hill climbing algorithm.

[0089] The following is an example to further illustrate the process of determining the target frequency resolution:

[0090] Based on the abnormal sound sample, it is known that the frequency band where the abnormal sound occurs, such as the stable single-frequency abnormal sound at frequencies fa, fb, and fc in the abnormal sound sample.

[0091] S1. Assuming that the initial frequency resolution is 1 Hz, increase the frequency resolution to 32 Hz, perform FFT on the abnormal sound sample, and perform abnormal sound detection based on the FFT.

[0092] S2. If the frequency resolution of 32 Hz can accurately identify the stable single-frequency abnormal sound at frequencies fa, fb, and fc in the abnormal sound sample, it means that increasing the frequency resolution to 32 Hz is sufficient to determine the abnormal sound. If the frequency resolution of 32 Hz cannot accurately identify the abnormal sound at all frequencies fa, fb, and fc in the abnormal sound sample and / or there is a misjudgment (for example, identifying other frequencies as abnormal sound), it means that although increasing the frequency resolution to 32 Hz reduces the calculation amount, it is still not sufficient to determine all active noise reduction abnormal sound points, and the frequency resolution needs to be reduced.

[0093] S3. If the frequency resolution is sufficient to determine the abnormal sound, select 32 Hz as the frequency resolution and determine the corresponding calculation amount, which is 1 / 32 of the initial calculation amount when the frequency resolution is 1 Hz. If the frequency resolution is not sufficient to determine all active noise reduction abnormal sound points, repeat step 2 until the frequency resolution that accurately identifies the abnormal sound and has the smallest calculation amount for abnormal sound detection is found.

[0094] Step 103, in response to the difference between the total spectral power amplitude and the filtered power being greater than or equal to the difference threshold, determining that the active noise reduction system has a stable single-frequency abnormal sound; wherein the filtered power is the result of filtering the noise peak from the total spectral power amplitude.

[0095] In this embodiment, by performing spectral analysis on the first noise signal, the total spectral power amplitude and the noise peak related to the abnormal sound in the first noise signal are determined, and then the abnormal sound can be accurately identified based on the total spectral power amplitude and the noise peak, thereby improving the accuracy of the active noise reduction system.

[0096] In an embodiment, in response to the difference between the total spectral power amplitude and the noise peak being less than the difference threshold, it is determined that the active noise reduction system has a stable single-frequency abnormal sound.

[0097] The difference threshold can be an empirical value, for example, 4 dB. When the active noise reduction system is working, if the total spectral power amplitude E of the first noise signal is measuredA 65dB(A), the first noise signal is composed of the sum of sound sources at all frequencies within 0-6000Hz. Through spectral analysis, the largest peak in the spectrum is at 500Hz, and the size of the noise peak E P is 62dB(A). E A and E P are converted into power respectively, 65dB: power1=10 (0.1*65) =3162277; 62dB: power2=10 (0.1*62) =1584893; power2 / power1≈0.5=50%. At this time, the sound at 500Hz accounts for 50% of the total power of the spectrum, and the power of other sound frequencies accounts for only 50%. Therefore, in the frequency spectrum of this sound, the sound at 500Hz is significantly greater than the sound at other frequencies. At this time, the active noise reduction system will emit a very unpleasant single-frequency sound, i.e. an abnormal sound, if the sound is in dB units, E A -E P =65-62=3dB<ΔE PT , it is determined that there is a stable single-frequency abnormal sound.

[0098] The difference threshold can also be dynamically selected according to actual conditions to improve the accuracy of abnormal sound detection. The following describes a way to achieve dynamic selection of the difference threshold, see Figure 4 , the difference threshold is determined by the following method:

[0099] Step 100-11, obtaining an abnormal signal and a normal sample.

[0100] The abnormal sample is a noise signal containing abnormal sound, and the normal sample is a noise signal not containing abnormal sound. The abnormal sample can be collected by experiment, or it can be the result of superimposing a noise signal with a frequency and amplitude matching the abnormal sound on the normal sample.

[0101] The number of abnormal samples and normal samples can be set according to actual needs. Understandably, the more samples, the better the difference threshold optimization.

[0102] Step 100-12, constructing a target function.

[0103] The target function is constructed according to at least one of the following parameters: the number of missed abnormal samples, the number of false normal samples, the proportion of missed abnormal samples, and the proportion of false normal samples.

[0104] The design principle of the objective function is to find a combination of parameters and a value, so that the difference threshold obtained by optimization can detect all abnormal sounds, and the normal signal is not misjudged as abnormal sound, and has good robustness. The objective function of parameter optimization can be set as: (1) (the number of missed abnormal samples + the number of misjudged normal samples) / the total number of samples, in which case the smaller the objective function is, the better; (2) (the abnormal sound judgment intensity of normal samples) / (the abnormal sound judgment intensity of abnormal signals), in which case the smaller the objective function is, the better.

[0105] wherein the abnormal sound judgment intensity = the degree to which the abnormal sample / normal sample meets the abnormal sound judgment. If the total abnormal sound threshold is 100, and the value is higher than 100 is abnormal sound, if the value of the normal sample is 30, then the abnormal sound intensity is 0.3, which is the abnormal sound judgment intensity of the normal sample. The smaller the value is, the farther the normal sound is from the standard of abnormal sound, the greater the probability of normal sound is, and the higher the accuracy of the detection algorithm is. If the value of the abnormal sound sample is 200, then the abnormal sound intensity is 2, which is the abnormal sound judgment intensity of the abnormal signal. The greater the value is, the more likely the sound is abnormal sound, the greater the probability of abnormal sound is, and the higher the accuracy of the detection algorithm is. Therefore, the smaller the value of (the abnormal sound judgment intensity of normal samples) / (the abnormal sound judgment intensity of abnormal signals) is, the higher the accuracy of the detection algorithm is, and the better the combination of parameters used for detection is.

[0106] The abnormal sound judgment intensity can also be defined in other ways, for example: the abnormal sound judgment intensity of normal samples = the number of normal samples misjudged as abnormal sound / the total number of normal samples; the abnormal sound judgment intensity of abnormal signals = the number of abnormal samples accurately judged as abnormal sound / the total number of abnormal samples.

[0107] In addition to the above objective function, other calculation formulas similar to the design principle of the objective function can also be used, and the embodiments of the present disclosure are not particularly limited.

[0108] Step 100-13: optimizing the objective function in the preset optimization range of the difference threshold value to determine the final difference threshold value.

[0109] In one embodiment, the preset optimization range is [1dB, 10dB], that is, the difference threshold value is searched for the optimal solution in [1dB, 10dB] to make the objective function minimum.

[0110] In this embodiment, the optimization range of the difference threshold value is provided, so that the optimization meets certain physical characteristics of abnormal sound, and the optimization accuracy and efficiency can be improved.

[0111] 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, hill climbing algorithm.

[0112] In one embodiment, it is determined that the active noise reduction system has the steady single-frequency abnormal sound in response to the ratio of the first decibel value to the second decibel value being less than a ratio threshold value. The first decibel value is a dB conversion result of the total amplitude of the spectrum power, and the second decibel value is a dB conversion result of the noise peak value.

[0113] Similar to the difference threshold value, the ratio threshold value ΔE PT may be an empirical value. If the first decibel value of the total amplitude of the spectrum power is 3.16x10 6 , and the second decibel value of the noise peak value is 1.58x10 6 , then E A / E P = 3.16x10 6 / 1.58x10 6 = 2 < ΔE PT , it is determined that the steady single-frequency abnormal sound exists.

[0114] The embodiment provides another judgment condition for abnormal sound detection, in which the ratio threshold value can be dynamically selected by target optimization to improve the accuracy of abnormal sound detection. The target optimization process of the ratio threshold value is similar to that of the difference threshold value, which will not be described herein again.

[0115] In one embodiment, the step of determining the total amplitude of the spectrum power and the noise peak value of the first noise signal according to the Fourier transform result comprises: performing A-weighting processing on the Fourier transform result, and determining the total amplitude of the spectrum power and the noise peak value of the first noise signal according to the A-weighting processing result.

[0116] Currently, the definition of abnormal sound is mainly based on the subjective feeling of a person, so some sounds of some frequencies can be ignored even if they are loud, because the human ear is not sensitive to the sound of this frequency band (such as low-frequency sound). In the abnormal sound detection process, the Fourier transform result of the first noise signal is first subjected to A-weighting processing based on the sensitivity of the human ear, and then abnormal sound detection is performed on the A-weighting processing result. Since the A-weighting processing can better reflect the hearing perception of the human ear, this detection method can more easily detect the real abnormal sound, has better detection effect, and is more in line with the feeling and judgment of the user on the abnormal sound.

[0117] Referring to Figure 5 , the green solid line is the spectrum diagram of the first noise signal without A-weighting processing, in which there are many low-frequency components that are not sensitive to the human ear below 100 Hz. The sound signal of the first noise signal after A-weighting processing is shown by the red dotted line in Figure 5 , and the total power ratio of the low-frequency components that are not sensitive to the human ear is reduced. After A-weighting processing, the abnormal sound detection is more accurate and more in line with the feeling of the human ear.

[0118] In one embodiment, the step 103 of detecting the abnormal sound by multiple times of judgment comprises: determining that the active noise reduction system has the stable single-frequency abnormal sound in response to the number of occurrences of the difference between the total amplitude of the spectral power and the noise peak value being less than the difference threshold value being greater than a number threshold value.

[0119] When the spectral data of the first noise signal is used for abnormal sound detection, the first noise signal collected may contain noise interference signals, or the first noise signal used for detection is not a complete steady-state signal, and has certain fluctuation characteristics over time. These reasons cause differences in the spectral data obtained at different times, and further cause misjudgment or false judgment phenomena in abnormal sound detection.

[0120] In order to reduce or avoid misjudgment and false judgment, in the embodiment, each time the difference between the total amplitude of the spectral power and the noise peak value is less than the difference threshold value, it is determined that the active noise reduction system has an abnormality, and the number of occurrences of the abnormality is counted by 1. When the number of occurrences is greater than a number threshold value, it is determined that the active noise reduction system has the stable single-frequency abnormal sound. The multiple times of judgment can effectively reduce or avoid misjudgment and false judgment.

[0121] The number threshold value can be set according to actual needs, can be an empirical value, for example, set to 3 times, or can be dynamically selected by target optimization to improve the accuracy of abnormal sound detection. The target optimization process of the number threshold value is similar to that of the difference threshold value, which will not be described here.

[0122] Many abnormal sound frequencies have certain fluctuations, and the fluctuations have a greater impact on the amplitudes of the corresponding frequencies. When the spectrum is used for abnormal sound recognition, there are misjudgments and omissions. Moreover, the human perception and judgment of abnormal sound are the overall perception of the power in a frequency band, rather than the power at a single frequency. Therefore, integrating the noise spectrum according to a certain rule and determining the abnormal sound according to the power of the corresponding frequency band after integration is more consistent with the actual human perception.

[0123] Based on this, in one embodiment, the Fourier transform result (spectrum) of the first noise signal is processed in segments to obtain at least two noise segments, and the total amplitude of the spectral power and the noise peak value are determined according to the integration of each noise segment. The total amplitude of the spectral power and the noise peak value here are the total amplitude of the spectral power and the noise peak value of the first noise signal globally.

[0124] The segmentation algorithm can be, but is not limited to, one of the following: 1 / n octave method, Bark bandwidth method, equal bandwidth method, etc. The first two methods are preferred, and n in the 1 / n octave method is a non-zero natural number such as 1, 2, 3, etc., which takes into account the detection accuracy and computing power, and n=3 is preferred.

[0125] In one embodiment, the heterophonic detection is performed on each noise segment respectively, specifically, the Fourier transform result (spectrum) of the first noise signal is segmented, the total amplitude of the spectrum power and the noise peak of each noise segment are calculated respectively, and the heterophonic detection is performed on each noise segment. The total amplitude of the spectrum power and the noise peak here are the local total amplitude of the spectrum power and the noise peak of each noise segment.

[0126] In one embodiment, before the Fourier transform result of the first noise signal is segmented, the Fourier transform result is subjected to A-weighting processing, and then the A-weighting processing result is segmented.

[0127] The device equipped with the active noise reduction system, such as the extractor hood, may also generate a sound with a spectrum similar to the stable single-frequency sound caused by the active noise reduction system, which is referred to as “normal heterophonic sound”. Although the “normal heterophonic sound” may also make the user 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, and the sound cannot be reduced by turning off or adjusting the active noise reduction system.

[0128] Based on this, in one embodiment, if the detection result of step 103 is that there is a stable single-frequency heterophonic sound, in order to exclude that the heterophonic sound is caused by the device equipped with the active noise reduction system rather than the active noise reduction system, the active noise reduction system is first turned off, a second noise signal during the process of turning off the active noise reduction system is obtained, the Fourier transform of the second noise signal is performed, the total amplitude of the spectrum power and the noise peak are calculated, and it is determined whether there is still a stable single-frequency heterophonic sound; if the determination result is that there is a stable single-frequency heterophonic sound in the second noise signal, and the heterophonic frequency of the second noise signal is the same as or similar to that of the first noise signal, it is determined that the heterophonic 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 heterophonic sound.

[0129] Another implementation mode of excluding the interference of the device heterophonic sound on the active noise reduction system heterophonic sound detection is provided below.

[0130] In one embodiment, referring to Figure 6 , before the first noise signal during the operation of the active noise reduction system is obtained, the method further includes:

[0131] Step 100, in the case where the active noise reduction system is not started, a second noise signal of a noise source is obtained, the second noise signal is subjected to segmentation processing, so that the power proportion of each noise segment of the second noise signal is less than a proportion threshold, and a segmentation strategy of the second noise signal is determined.

[0132] The second noise signal is collected by a first microphone.

[0133] The number of noise segments obtained by segmenting the first noise signal is determined according to the power proportion of each noise segment, and if the power proportion of each noise segment is not less than the proportion threshold, re-segmentation processing is required. The frequency bands of each noise segment can be the same or different. The second noise signal is segmented so that the power proportion of each noise segment of the second noise signal is less than the proportion threshold, thereby excluding the influence of the frequency band characteristics of the range hood itself on the determination of the abnormal sound.

[0134] The proportion threshold can be set according to actual conditions, and the value range of the proportion threshold can be [40%, 50%] for example. Preferably, the proportion threshold is 40%. The value 40% is selected to leave a margin to avoid misjudgment caused by being too close to 50%, because the criterion for subsequent determination of abnormal sound is that more than 50% of the proportion threshold is determined as stable single-frequency abnormal sound. Since it cannot be guaranteed that the power proportion of a noise segment is an accurate value, there can be fluctuations of up to 5% up and down, so if it is too close to 50%, the fluctuation error can cause misjudgment of abnormal sound. By setting the proportion threshold to 40%, when the first power proportion is less than 40%, it indicates that the power of the noise segment has been reduced to a sufficiently low level to avoid misjudgment caused by power fluctuations of the noise segment.

[0135] The segmentation strategy includes the following parameters: the number of segments, the upper and lower limit frequencies of each noise segment.

[0136] The calculation method of the power proportion is introduced below, and the formula is as follows:

[0137] H=1-e i / E;

[0138]

[0139] wherein H represents the power proportion; e i represents the power corresponding to the i-th noise segment in the noise signal; E represents the sum of the powers of each noise segment contained in the noise signal; X j (t) represents the amplitude of the j-th sampling point of the noise signal in the time domain; N represents the number of sampling points of the noise segment contained in the noise signal. e i The calculation method of E is similar.

[0140] In step 102, the first noise signal is segmented according to the segmentation strategy, and the noise segments obtained by segmentation are subjected to Fourier transform, or the Fourier transform results are segmented according to the segmentation strategy. Subsequently, the global frequency spectrum power total amplitude and the noise peak value of the first noise signal are determined according to the integral of each noise segment, or the frequency spectrum power total amplitude and the noise peak value of each noise segment, i.e. the local frequency spectrum power total amplitude and the noise peak value, are determined.

[0141] In this embodiment, the abnormal sound of the active noise reduction system can be quickly monitored, the influence of the frequency band characteristics of the range hood itself on the abnormal sound judgment is excluded, and the active noise reduction abnormal sound of the range hood is accurately monitored.

[0142] Another implementation mode for excluding the interference of equipment abnormal sound on the abnormal sound detection of the active noise reduction system is provided below.

[0143] In one embodiment, referring to Figure 7 , before acquiring the first noise signal in the running process of the active noise reduction system, the method further includes:

[0144] Step 100-21, acquiring a second noise signal of the noise source in the case that the active noise reduction system is not started.

[0145] Step 100-22, performing octave calculation on the second noise signal to determine a segmentation strategy matched with the second noise signal.

[0146] n in the 1 / n octave is a non-zero natural number such as 1, 2, 3, etc., which takes into account the detection accuracy and computing power, and n=3 is preferred.

[0147] Step 100-23, segmenting the second noise signal according to the segmentation strategy and calculating the characteristic value of each noise segment of the second noise signal.

[0148] The characteristic value includes power and / or decibel value.

[0149] Step 100-24, determining a target gain of a first target noise segment with a characteristic value greater than a characteristic value threshold.

[0150] The characteristic value of the first target noise segment after being filtered by the band-pass filter with the target gain is less than or equal to the characteristic value threshold.

[0151] The characteristic value of the first target noise segment is greater than the characteristic value threshold, indicating that the first target noise segment contains abnormal sound, and the active noise reduction system is not started when the first target noise segment is collected, meaning that the abnormal sound contained in the first target noise segment is caused by the equipment carrying the active noise reduction system, rather than by the active noise reduction system. In order to exclude the interference of equipment abnormal sound in abnormal sound detection, the second target noise segment contained in the first noise signal needs to be filtered to filter the abnormal sound part caused by the equipment. The frequency band of the second target noise segment contains the frequency band of the first target noise segment.

[0152] Taking the characteristic value as the decibel value as an example, it is assumed that the decibel value of the first target noise segment before filtering is P, and the decibel value after filtering by the target gain band-pass filter is P' (less than or equal to the characteristic value threshold). Here, P' is set to be greater than the adjacent frequency band decibel value by 1 dB. According to the formula: P' = 20lg(E' / E0), the power E' of the first target noise segment after filtering can be calculated. By knowing the power E of the first target noise segment before filtering and the power E' after filtering, the target gain / through rate of the first target noise segment is calculated as a = E' / E.

[0153] It should be noted that P' is set to be greater than the adjacent frequency band decibel value by 1 dB, rather than being equal, in order to make the abnormal sound monitoring more sensitive. Compared with the latter, the former is more sensitive to abnormal sound judgment. When the judgment condition is 3 dB higher than the adjacent frequency band, it requires less power to judge the abnormal sound than when it is the same as the adjacent frequency band. At this time, the abnormal sound judgment sensitivity is higher, while the accuracy is also guaranteed.

[0154] In step 102, the first noise signal is segmented to obtain at least two noise segments, and a target gain band-pass filter is used to filter the second target noise segment contained in the first noise signal. The noise segments obtained by segmenting the first noise signal and the filtered noise segments are subjected to Fourier transform, respectively.

[0155] Among them, the noise segments obtained by segmenting the first noise signal are other noise segments except the noise segment subjected to filtering.

[0156] The target gain band-pass filter is used to filter the second target noise segment, that is, the amplitude of the second target noise segment is multiplied by a to filter the abnormal sound part caused by the device in the second target noise segment. The amplitudes of other noise segments in the second noise signal except the second target noise segment are not subjected to attenuation processing, and the power through rate of other noise segments is 100%.

[0157] In this embodiment, the target gain band-pass filter is used to filter the noise segments of the target noise segments in the first 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.

[0158] The disclosure also provides a control method of an active noise reduction system, which comprises: detecting abnormal sound of the active noise reduction system according to the abnormal sound detection method provided in any of the above embodiments; and restarting the active noise reduction system in response to the existence of stable single-frequency abnormal sound of the active noise reduction system.

[0159] According to experience, the abnormal sound of the active noise reduction system is caused by some faults or interference. Restarting the active noise reduction system can weaken or eliminate the faults or interference, improve the effectiveness of the active noise reduction system, and thus improve the user experience.

[0160] Corresponding to the foregoing abnormal sound detection method embodiments, the present disclosure also provides embodiments of abnormal sound detection devices.

[0161] Figure 8 A module schematic diagram of an abnormal sound detection device provided for an exemplary embodiment of the present disclosure is provided, and the device comprises:

[0162] The acquisition module 81 is configured to acquire a first noise signal during operation of the active noise reduction system; the first noise signal is a noise signal of a noise source collected by the microphone or a control signal of the loudspeaker.

[0163] The determination module 82 is configured to perform Fourier transform on the first noise signal and determine a total spectral power amplitude and a noise peak value of the first noise signal according to the Fourier transform result.

[0164] The detection module 83 is configured to determine that the active noise reduction system has a stable single-frequency abnormal sound in response to a difference between the total spectral power amplitude and the filtered power being greater than or equal to a difference threshold value; the filtered power is a result of filtering the noise peak value from the total spectral power amplitude.

[0165] Optionally,

[0166] The device further comprises the difference threshold value determination module, which is configured to:

[0167] Acquire abnormal samples and normal samples; the abnormal samples are noise signals containing abnormal sounds, and the normal samples are noise signals not containing abnormal sounds.

[0168] Construct a target function; the target function is constructed according to at least one of the following parameters: the number of missed abnormal samples, the number of false normal samples, the proportion of missed abnormal samples, and the proportion of false normal samples.

[0169] Optimize the target function within a preset optimization range of the difference threshold value to determine the final difference threshold value.

[0170] Optionally, the preset optimization range is [1dB, 10dB];

[0171] And / or, the optimization algorithm includes at least one of the following algorithms: genetic algorithm, simulated annealing algorithm, particle swarm optimization algorithm, Bayesian optimization algorithm, and hill climbing algorithm.

[0172] And / or, the target function is the ratio of the number of abnormalities to the total number of samples, and the number of abnormalities is the sum of the number of missed abnormal samples and the number of false normal samples.

[0173] Optionally, if the spectrum of the first noise signal contains at least two peak values, the largest peak value is determined as the noise peak value.

[0174] and / or, acquiring a first noise signal in the operation of the active noise reduction system at a target sampling frequency; the target sampling frequency is determined according to an abnormal sound frequency and / or the calculation capacity of the analysis device of the first noise signal; the abnormal sound frequency is determined according to a noise sample containing abnormal sound;

[0175] and / or, performing Fourier transform on the first noise signal at a target frequency resolution; the target frequency resolution is obtained by optimization, and the optimization target is to accurately identify abnormal sound and minimize the calculation amount of abnormal sound detection.

[0176] Optionally, the determining module is specifically configured to perform A-weighting processing on the Fourier transform result, and determine the total spectral power amplitude and the noise peak value of the first noise signal according to the A-weighting processing result.

[0177] and / or, the determining module is specifically configured to divide the Fourier transform result into at least two noise segments, and determine the total spectral power amplitude and the noise peak value according to the integral of each noise segment.

[0178] and / or, the detecting module is specifically configured to determine that the active noise reduction system has stable single-frequency abnormal sound in response to the occurrence frequency of the difference between the total spectral power amplitude and the noise peak value being less than a difference threshold value being greater than a frequency threshold value.

[0179] Optionally, the acquiring module is further configured to acquire a second noise signal of a noise source when the active noise reduction system is not started.

[0180] The device further includes a segmenting module configured to perform segmenting processing on the second noise signal, so that the power proportion of each noise segment of the second noise signal is less than a proportion threshold value, and determine a segmenting strategy of the second noise signal.

[0181] The determining module is specifically configured to perform segmenting processing on the first noise signal according to the segmenting strategy, and perform Fourier transform on the noise segment obtained by segmenting.

[0182] Optionally, the acquiring module is further configured to acquire a second noise signal of a noise source when the active noise reduction system is not started.

[0183] The device further includes a calculating module configured to perform octave calculation on the second noise signal to determine a segmenting strategy matched with the second noise signal.

[0184] The segmenting module is configured to perform segmenting processing on the second noise signal according to the segmenting strategy, and calculate characteristic values of each noise segment of the second noise signal; the characteristic values include power and / or decibel value.

[0185] The gain module is configured to determine a target gain of the first target noise segment whose feature value is greater than a feature value threshold; and filter the first target noise segment by a band-pass filter with the target gain, so that the feature value of the first target noise segment after the filtering is less than or equal to the feature value threshold.

[0186] The determining module is specifically configured to:

[0187] The first noise signal is segmented to obtain at least two noise segments, and a second target noise segment contained in the first noise signal is filtered by a band-pass filter with a target gain, and the noise segments obtained by the segmentation and the filtered noise segment are respectively subjected to Fourier transform; the frequency band of the second target noise segment contains the frequency band of the first target noise segment.

[0188] For the device embodiment, since it basically corresponds to the method embodiment, the related parts can be referred to the part of the method embodiment. The device embodiment described above is only illustrative, and the units described as separate components can or can not be physically separated, and the components of the unit can or can not be physical units, i.e., they can be located in one place or distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the present disclosure.

[0189] Figure 9 A structure diagram of an active noise reduction system is shown for an example embodiment of the present disclosure, which includes a memory, a processor, and a computer program stored on the memory and used to run on the processor, and the processor implements the hetero-sound detection method or the control method of any of the above embodiments when executing the computer program. Figure 9 The displayed active noise reduction system 90 is only an example, and should not bring any limitation to the function and use range of the embodiments of the present disclosure.

[0190] As Figure 9 shown, the components of the active noise reduction system 90 can include but are not limited to the at least one processor 91, the at least one memory 92, and the bus 93 connecting different system components including the memory 92 and the processor 91.

[0191] The bus 93 includes a data bus, an address bus, and a control bus.

[0192] The memory 92 can include a 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.

[0193] The memory 92 can also include a program tool 925 (or utility) having a set (at least one) of 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 implementation of a network environment.

[0194] The processor 91 performs various function applications and data processing by running the computer programs stored in the memory 92, such as the heterophonic sound detection method or the control method provided by any of the above embodiments.

[0195] The active noise reduction system 90 can also communicate with one or more external devices 94 (such as a range hood processor, etc.). Such communication can be through the input / output (I / O) interface 95. Also, the active noise reduction system 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) through a network adapter 96. As shown, the network adapter 96 communicates with the other modules of the active noise reduction system 90 through the bus 93. It should be appreciated that, although not shown, other hardware and / or software modules can be used in conjunction with the active noise reduction system 90, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID (Redundant Array of Independent Disks) systems, tape drives, and data backup storage systems, etc.

[0196] It should be noted that, although several units / modules or sub-units / modules of the active noise reduction system are mentioned in the above detailed description, such division is merely exemplary and not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided into multiple units / modules.

[0197] The embodiments of the present disclosure also provide a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the heterophonic sound detection method or the control method provided by any of the above embodiments.

[0198] More specifically, the 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.

[0199] The embodiments of the present disclosure also provide a computer program product, which includes a computer program, and the computer program is executed by a processor to implement the heterophonic sound detection method or the control method provided by any of the above embodiments.

[0200] program code for carrying out a 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 a user device, executed partly on a user device and partly on a remote device, executed as a stand-alone software package, partly on a user device and partly on a remote device, or entirely on a remote device.

[0201] Although the specific embodiments of the present disclosure are described above, those skilled in the art should understand that this is only an illustration, 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 these 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 of detecting a squeal sound, characterized by, The application is applied to an active noise reduction system, the active noise reduction system comprising a microphone and a speaker; the abnormal sound detection method comprises: obtaining a first noise signal in the operation process of the active noise reduction system; the first noise signal comprises a noise signal of a noise source collected by the microphone and / or a control signal of the speaker; performing Fourier transform on the first noise signal, and determining a total spectral power amplitude and a noise peak value of the first noise signal according to the Fourier transform result; in response to the difference between the total spectral power amplitude and the filtered power being greater than or equal to a difference threshold value, determining that the active noise reduction system has a stable single-frequency abnormal sound; wherein the filtered power is the result of filtering the noise peak value from the total spectral power amplitude.

2. The abnormal sound detection method according to claim 1, characterized by, The difference threshold value is determined by the following method: obtaining abnormal samples and normal samples; the abnormal samples are noise signals containing abnormal sounds, and the normal samples are noise signals not containing abnormal sounds; constructing an objective function; the objective function is constructed according to at least one of the following parameters: the number of missed abnormal samples, the number of false normal samples, the proportion of missed abnormal samples, and the proportion of false normal samples; optimizing the objective function in a preset optimization range of the difference threshold value to determine the final difference threshold value.

3. The abnormal sound detection method according to claim 2, characterized by, The preset optimization range is [1dB, 10dB]; and / or, the optimization algorithm comprises at least one of the following algorithms: genetic algorithm, simulated annealing algorithm, particle swarm algorithm, Bayesian optimization algorithm, hill climbing algorithm; and / or, the objective function is the ratio of the number of abnormalities to the total number of samples, and the number of abnormalities is the sum of the number of missed abnormal samples and the number of false normal samples.

4. The abnormal sound detection method according to claim 1, characterized by, If the spectrum of the first noise signal contains at least two peak values, the largest peak value is determined as the noise peak value; and / or, the first noise signal in the operation process of the active noise reduction system is obtained at a target sampling frequency; the target sampling frequency is determined according to the abnormal sound frequency and / or the computing power of the analysis device of the first noise signal; the abnormal sound frequency is determined according to the noise sample containing abnormal sound; and / or, the Fourier transform of the first noise signal is performed at a target frequency resolution; The target frequency resolution is obtained by optimization, and the optimization target is to accurately identify abnormal sound and minimize the calculation amount of abnormal sound detection.

5. The abnormal sound detection method according to claim 1, characterized by, According to the Fourier transform result, the total spectral power amplitude and the noise peak value of the first noise signal are determined, which comprises: performing A-weighting processing on the Fourier transform result, and determining the total spectral power amplitude and the noise peak value of the first noise signal according to the A-weighting processing result; and / or, the Fourier transform result is divided into at least two noise segments, and the total spectral power amplitude and the noise peak value are determined according to the integration of each noise segment; and / or, in response to the difference between the total spectral power amplitude and the noise peak value being less than the difference threshold value, it is determined that the active noise reduction system has a stable single-frequency abnormal sound, which comprises: in response to the number of times that the difference between the total spectral power amplitude and the noise peak value is less than the difference threshold value being greater than a number threshold value, it is determined that the active noise reduction system has a stable single-frequency abnormal sound.

6. The abnormal sound detection method according to any one of claims 1 to 5, characterized by, Before obtaining the first noise signal in the operation process of the active noise reduction system, it further comprises: In the case that the active noise reduction system is not started, a second noise signal of the noise source is acquired; The second noise signal is segmented to make the power proportion of each noise segment of the second noise signal less than a proportion threshold, and a segmentation strategy of the second noise signal is determined; The first noise signal is subjected to Fourier transform, including: The first noise signal is segmented according to the segmentation strategy, and the noise segments obtained by segmentation are subjected to Fourier transform.

7. The abnormal sound detection method according to any one of claims 1 to 5, characterized by, Before acquiring the first noise signal in the running process of the active noise reduction system, the method further includes: In the case that the active noise reduction system is not started, a second noise signal of the noise source is acquired; The second noise signal is subjected to octave calculation to determine a segmentation strategy matched with the second noise signal; The second noise signal is segmented according to the segmentation strategy, and characteristic values of each noise segment of the second noise signal are calculated; the characteristic values include power and / or decibel value; A target gain of a first target noise segment with a characteristic value greater than a characteristic value threshold is determined; the characteristic value of the first target noise segment after the first target noise segment is filtered by a band-pass filter of the target gain is less than or equal to the characteristic value threshold; The first noise signal is subjected to Fourier transform, including: The first noise signal is segmented to obtain at least two noise segments, a second target noise segment contained in the first noise signal is filtered by a band-pass filter of a target gain, and the noise segments obtained by segmentation and the filtered noise segments in the first noise signal are respectively subjected to Fourier transform; a frequency band of the second target noise segment contains a frequency band of the first target noise segment.

8. A control method of an active noise reduction system, characterized by, The method includes: The active noise reduction system is subjected to the abnormal sound detection method according to any one of claims 1-7; In response to the active noise reduction system having a stable single-frequency abnormal sound, the active noise reduction system is restarted.

9. A squeal detection device characterized by comprising: The abnormal sound detection method is applied to an active noise reduction system, the active noise reduction system including a microphone and a speaker; the abnormal sound detection device is used to implement the abnormal sound detection method according to any one of claims 1-7; The abnormal sound detection method includes: An acquisition module is configured to acquire a first noise signal in a running process of the active noise reduction system; the first noise signal is a noise signal of a noise source collected by the microphone or a control signal of the speaker; A determination module is configured to subject the first noise signal to Fourier transform, and determine a total amplitude of spectral power and a noise peak value of the first noise signal according to a Fourier transform result; A detection module is configured to determine that the active noise reduction system has a stable single-frequency abnormal sound in response to a difference between the total amplitude of spectral power and the noise peak value being less than a difference threshold.

10. An active noise reduction system comprising a memory, a processor, and a computer program stored on the memory for running on the processor, characterized in that, The processor implements the method according to any one of claims 1-8 when executing the computer program.

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