Howling detection frequency optimization method, device, equipment and storage medium

Abstract

The present invention discloses a method, device, equipment, and storage medium for optimizing howling detection frequencies. The method comprises: obtaining a test voice signal and converting the test voice signal into a digital signal to obtain a signal to be processed; generating a target spectrum corresponding to the howling detection frequency based on the signal to be processed, and determining the howling point in the target spectrum to obtain the howling detection frequency; calibrating the howling detection frequency according to the target spectrum to obtain a calibrated howling frequency; and using the calibrated howling frequency as the attenuation frequency corresponding to a notch filter in the device to be optimized to achieve howling suppression and complete the optimization of the howling detection frequency. The present invention not only accurately obtains the howling frequency, but also greatly reduces the amount of computation, achieving optimal adaptation of notch processing.

Description

Technical Field

[0001] The present invention relates to the technical field of sound signal processing, and in particular to a method, device, equipment and storage medium for optimizing a howling detection frequency point. Background Art

[0002] In professional audio engineering, from theaters to conference rooms, from stadiums to karaoke bars, howling caused by acoustic feedback is a common problem in every type of sound reinforcement system. Howling processing typically requires detecting the howling point. Traditional methods for refining the howling frequency either require excessive computational effort or suffer from significant estimation errors. Local voice pickup often incorporates ambient noise, resulting in significant errors in howling frequency estimation. Furthermore, in notch filtering, the bandwidth of the notch filter is designed to be calculated in octaves. Large low-frequency deviations can render howling suppression ineffective. Summary of the Invention

[0003] The purpose of the present invention is to provide a method, device, equipment and storage medium for optimizing the frequency point of howling detection, which solves the problems existing in the prior art.

[0004] The present invention is achieved through the following technical solutions:

[0005] In a first aspect, the present invention provides a method for optimizing a howling detection frequency point, comprising:

[0006] Acquire a test voice signal and convert the test voice signal into a digital signal to obtain a signal to be processed, wherein the test voice signal is used to represent the voice signal collected in real time by the device to be optimized;

[0007] Based on the signal to be processed, a target spectrum corresponding to the howling detection frequency point is generated, and the howling point in the target spectrum is determined to obtain the howling detection frequency point;

[0008] Calibrate the howling detection frequency point according to the target spectrum to obtain a calibrated howling frequency point;

[0009] The calibrated howling frequency point is used as the attenuation frequency point corresponding to the notch filter in the device to be optimized, so as to achieve howling suppression and complete the optimization of the howling detection frequency point.

[0010] In a possible implementation, generating a target spectrum corresponding to a howling detection frequency point based on the signal to be processed includes:

[0011] The signal to be processed is downsampled according to multiples of 1, 2, 4 and 8 to obtain 4 segments of initial signals;

[0012] Perform 512-point FFT processing on the four initial signals to obtain a first spectrum F1, a second spectrum F2, a third spectrum F3, and a fourth spectrum F4;

[0013] The first spectrum F1, the second spectrum F2, the third spectrum F3 and the fourth spectrum F4 are combined to obtain a target spectrum corresponding to the howling detection frequency point;

[0014] The peak value in the target spectrum corresponding to the howling detection frequency point is tracked to obtain the howling detection frequency point.

[0015] In a possible implementation, combining the first spectrum F1, the second spectrum F2, the third spectrum F3, and the fourth spectrum F4 includes:

[0016] Extract the frequency spectrum in the frequency range of 20 to 2343 Hz from the first spectrum F1 to obtain a first sub-spectrum;

[0017] Extract the frequency spectrum in the frequency range of 2343 to 4686 Hz from the second spectrum F2 to obtain a second sub-spectrum;

[0018] Extract the frequency spectrum in the frequency range of 4686 to 9375 Hz from the third spectrum F3 to obtain a third sub-spectrum;

[0019] Extract the frequency spectrum in the frequency range of 9375 to 20000 Hz from the fourth spectrum F4 to obtain a fourth sub-spectrum;

[0020] The first sub-spectrum, the second sub-spectrum, the third sub-spectrum, and the fourth sub-spectrum are combined into a spectrum in the frequency range of 20 Hz to 20 kHz to obtain a target spectrum corresponding to the howling detection frequency point.

[0021] In a possible implementation, calibrating the howling detection frequency point according to the target spectrum to obtain the calibrated howling frequency point includes:

[0022] For a certain howling detection frequency point X(f, k), determine the previous frequency point X(f, k+1) and the next frequency point X(f, k-1) of the howling detection frequency point in the target spectrum, where X(f, k) is used to represent the frequency corresponding to the frequency point k in the target spectrum, X(f, k+1) is used to represent the frequency corresponding to the frequency point k+1 in the target spectrum, and X(f, k-1) is used to represent the frequency corresponding to the frequency point k-1 in the target spectrum;

[0023] Obtain the correction coefficient corresponding to the howling detection frequency point based on the previous frequency point X(f, k+1) and the next frequency point X(f, k-1);

[0024] The howling detection frequency point is calibrated according to the correction coefficient to obtain a calibrated howling frequency point.

[0025] In a possible implementation, obtaining a correction coefficient corresponding to a howling detection frequency point based on a previous frequency point X(f, k+1) and a next frequency point X(f, k-1) includes:

[0026] Obtain the median between the previous frequency point X(f, k+1) and the next frequency point X(f, k-1), and obtain the first difference MDiff(f, k) between the howling detection frequency point X(f, k) and the median:

[0027] MDiff(f,k)=X(f,k)-0.5*(X(f,k+1)+X(f,k-1))

[0028] The second difference XDiff(f, k) between the previous frequency point X(f, k+1) and the next frequency point X(f, k-1) is obtained as:

[0029] XDiff(f,k)=X(f,k+1)-X(f,k-1)

[0030] A correction coefficient is determined according to the first difference MDiff(f,k) and the second difference XDiff(f,k).

[0031] In a possible implementation, the correction coefficient corr(f,k) is determined according to the first difference MDiff(f,k) and the second difference XDiff(f,k):

[0032]

[0033] Among them, real means taking the real part, and conj means taking the conjugate.

[0034] In a possible implementation manner, the howling detection frequency point is calibrated according to the correction coefficient as follows:

[0035]

[0036] in, Indicates the calibration howling frequency, FS indicates the sampling frequency of the device to be optimized, and NFFT indicates the number of points in FFT processing.

[0037] In a second aspect, the present invention provides a howling detection frequency optimization device, comprising a signal acquisition module, a howling detection module, a first optimization module, and a second optimization module;

[0038] The signal acquisition module is used to acquire a test voice signal and convert the test voice signal into a digital signal to obtain a signal to be processed, wherein the test voice signal is used to represent the voice signal collected in real time by the device to be optimized;

[0039] The howling detection module is used to generate a target spectrum corresponding to a howling detection frequency point based on the signal to be processed, and determine the howling point in the target spectrum to obtain the howling detection frequency point;

[0040] The first optimization module is configured to calibrate the howling detection frequency point according to the target spectrum to obtain a calibrated howling frequency point;

[0041] The second optimization module is configured to use the calibrated howling frequency point as the attenuation frequency point corresponding to the notch filter in the device to be optimized, thereby achieving howling suppression and completing the optimization of the howling detection frequency point.

[0042] In a third aspect, the present invention provides a device for optimizing frequency points for howling detection, including a processor and a memory;

[0043] The memory stores computer-executable instructions;

[0044] The processor executes the computer-executable instructions stored in the memory, so that the processor performs the howling detection frequency point optimization method as described in the first aspect.

[0045] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the howling detection frequency optimization method described in the first aspect.

[0046] The present invention provides a howling detection frequency optimization method, device, equipment and storage medium, which can reduce the howling frequency estimation error by identifying the howling point and then calibrating the howling point, and adapt the notch processing in an optimal manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the examples. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be considered as limiting the scope. A person of ordinary skill in the art can also derive other relevant drawings based on these drawings without inventive effort. In the drawings:

[0048] Figure 1 This is a flow chart of a method for optimizing frequency points for howling detection provided by an embodiment of the present invention.

[0049] Figure 2 A schematic structural diagram of a device for optimizing frequency points for howling detection provided by an embodiment of the present invention.

[0050] Figure 3 A schematic structural diagram of a howling detection frequency optimization device provided by an embodiment of the present invention.

[0051] Among them, 201 is a signal acquisition module, 202 is a howling detection module, 203 is a first optimization module, 204 is a second optimization module, 301 is a memory, 302 is a processor, and 303 is a bus. DETAILED DESCRIPTION

[0052] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with examples and drawings. The exemplary embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0053] Example 1

[0054] like Figure 1 As shown, this embodiment provides a method for optimizing the frequency point of howling detection, including:

[0055] S101: Acquire a test voice signal, and convert the test voice signal into a digital signal to obtain a signal to be processed, wherein the test voice signal is used to represent a voice signal collected in real time by a device to be optimized.

[0056] The execution subject of this embodiment may be a howling detection frequency optimization device, which may be implemented by software or by a combination of software and hardware.

[0057] Optionally, local voice may be picked up by a microphone of the device to be optimized, and then converted into a digital signal by an AD conversion circuit (Analog-to-Digital Convert, analog-to-digital conversion circuit), thereby obtaining a signal to be processed.

[0058] S102 : Based on the signal to be processed, generate a target spectrum corresponding to a howling detection frequency point, and determine a howling point in the target spectrum to obtain the howling detection frequency point.

[0059] Optionally, the howling detection frequency point can be obtained through segmented zoomFFT (spectrum refinement analysis method) processing, frequency band resynthesis and peak statistical tracking.

[0060] S103: Calibrate the howling detection frequency points according to the target spectrum to obtain calibrated howling frequency points.

[0061] Optionally, the howling detection frequency point can be corrected by using the center of gravity correction method to obtain a calibrated howling frequency point, so as to design a notch filter according to the calibrated howling frequency point and achieve a better howling elimination effect.

[0062] S104: Using the calibrated howling frequency point as the attenuation frequency point corresponding to the notch filter in the device to be optimized, howling suppression is achieved, and optimization of the howling detection frequency point is completed.

[0063] This embodiment provides a method for optimizing howling detection frequencies. By collecting sound signals and then performing segmented processing, frequency band synthesis, peak statistical tracking, and frequency correction, the method can accurately obtain howling frequencies. Compared with existing technologies, this method is less complex and reduces the amount of computation.

[0064] In a possible implementation, generating a target spectrum corresponding to a howling detection frequency point based on the signal to be processed includes:

[0065] The signal to be processed is downsampled according to multiples of 1, 2, 4 and 8 to obtain 4 segments of initial signals.

[0066] It is worth noting that, in addition to the downsampling multiple relationship described in this embodiment, other downsampling multiples may also be used to implement processing of the signal to be processed.

[0067] The four initial signals are processed by 512-point FFT (Fast Fourier Transform) to obtain a first spectrum F1, a second spectrum F2, a third spectrum F3 and a fourth spectrum F4.

[0068] The four initial signals are subjected to 512-point FFT processing, including: performing 512-point FFT processing on the initial signal with a downsampling multiple of 1 to obtain a first spectrum F1; performing 512-point FFT processing on the initial signal with a downsampling multiple of 2 to obtain a second spectrum F2; performing 512-point FFT processing on the initial signal with a downsampling multiple of 4 to obtain a third spectrum F3; performing 512-point FFT processing on the initial signal with a downsampling multiple of 8 to obtain a fourth spectrum F4.

[0069] The first spectrum F1, the second spectrum F2, the third spectrum F3 and the fourth spectrum F4 are combined to obtain a target spectrum corresponding to the howling detection frequency point.

[0070] The peak value in the target spectrum corresponding to the howling detection frequency point is tracked to obtain the howling detection frequency point.

[0071] In a possible implementation, combining the first spectrum F1, the second spectrum F2, the third spectrum F3, and the fourth spectrum F4 includes:

[0072] The frequency spectrum in the frequency range of 20 to 2343 Hz is extracted from the first spectrum F1 to obtain a first sub-spectrum.

[0073] The frequency spectrum in the frequency range of 2343 to 4686 Hz is extracted from the second spectrum F2 to obtain a second sub-spectrum.

[0074] The frequency spectrum in the frequency range of 4686 to 9375 Hz in the third spectrum F3 is extracted to obtain a third sub-spectrum.

[0075] The frequency spectrum in the frequency range of 9375 to 20000 Hz in the fourth frequency spectrum F4 is extracted to obtain a fourth sub-spectrum.

[0076] The first sub-spectrum, the second sub-spectrum, the third sub-spectrum, and the fourth sub-spectrum are combined into a spectrum in the frequency range of 20 Hz to 20 kHz to obtain a target spectrum corresponding to the howling detection frequency point.

[0077] In a possible implementation, calibrating the howling detection frequency point according to the target spectrum to obtain the calibrated howling frequency point includes:

[0078] For a certain howling detection frequency point X(f, k), the previous frequency point X(f, k+1) and the next frequency point X(f, k-1) of the howling detection frequency point are determined in the target spectrum, where X(f, k) is used to represent the frequency corresponding to the frequency point k in the target spectrum, X(f, k+1) is used to represent the frequency corresponding to the frequency point k+1 in the target spectrum, and X(f, k-1) is used to represent the frequency corresponding to the frequency point k-1 in the target spectrum.

[0079] Among them, k belongs to 0, 1, 2.....NFFT / 2.

[0080] According to the previous frequency point X(f, k+1) and the next frequency point X(f, k-1), the correction coefficient corresponding to the howling detection frequency point is obtained.

[0081] The howling detection frequency point is calibrated according to the correction coefficient to obtain a calibrated howling frequency point.

[0082] In a possible implementation, obtaining a correction coefficient corresponding to a howling detection frequency point based on a previous frequency point X(f, k+1) and a next frequency point X(f, k-1) includes:

[0083] Obtain the median between the previous frequency point X(f, k+1) and the next frequency point X(f, k-1), and obtain the first difference MDiff(f, k) between the howling detection frequency point X(f, k) and the median:

[0084] MDiff(f,k)=X(f,k)-0.5*(X(f,k+1)+X(f,k-1))

[0085] The second difference XDiff(f, k) between the previous frequency point X(f, k+1) and the next frequency point X(f, k-1) is obtained as:

[0086] XDiff(f,k)=X(f,k+1)-X(f,k-1)

[0087] A correction coefficient is determined according to the first difference MDiff(f,k) and the second difference XDiff(f,k).

[0088] In a possible implementation, the correction coefficient corr(f,k) is determined according to the first difference MDiff(f,k) and the second difference XDiff(f,k):

[0089]

[0090] Among them, real means taking the real part, and conj means taking the conjugate.

[0091] In a possible implementation manner, the howling detection frequency point is calibrated according to the correction coefficient as follows:

[0092]

[0093] in, Indicates the calibration howling frequency, FS indicates the sampling frequency of the device to be optimized, and NFFT indicates the number of points in FFT processing.

[0094] The device to be optimized does not use the feedback elimination provided by the present invention first, and increases the gain until the system begins to howl, and the gain at this time is calculated as G1; then the present invention is turned on, and the gain is increased until howling occurs, and the gain at this time is calculated as G2. The system increase gain is G=G1-G2. The system gain can be increased by 5-6dB by enabling the present invention.

[0095] Example 2

[0096] like Figure 2 As shown, this embodiment provides a howling detection frequency point optimization device, including a signal acquisition module 201 , a howling detection module 202 , a first optimization module 203 and a second optimization module 204 .

[0097] The signal acquisition module 201 is used to acquire a test voice signal and convert the test voice signal into a digital signal to obtain a signal to be processed. The test voice signal is used to represent the voice signal collected in real time by the device to be optimized.

[0098] The howling detection module 202 is configured to generate a target spectrum corresponding to a howling detection frequency point based on the signal to be processed, and determine a howling point in the target spectrum to obtain the howling detection frequency point.

[0099] The first optimization module 203 is configured to calibrate the howling detection frequency points according to the target spectrum to obtain calibrated howling frequency points.

[0100] The second optimization module 204 is configured to use the calibrated howling frequency point as the attenuation frequency point corresponding to the notch filter in the device to be optimized, thereby achieving howling suppression and completing the optimization of the howling detection frequency point.

[0101] This embodiment provides a device for optimizing the frequency point of howling detection, which can implement the method and technical solution described in Example 1. The principles and beneficial effects thereof are similar and will not be described in detail here.

[0102] Example 3

[0103] like Figure 3 As shown, this embodiment provides a howling detection frequency point optimization device, including a memory 301 and a processor 302 , and the memory 301 and the processor 302 are connected to each other via a bus 303 .

[0104] The memory 301 stores computer-executable instructions.

[0105] The processor 302 executes the computer-executable instructions stored in the memory, so that the processor performs the method for optimizing the frequency point of howling detection as described in embodiment 1.

[0106] For example, the memory may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out memory (FIFO), and / or first-in-last-out memory (FILO). Specifically, the processor may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor may be implemented in at least one hardware form selected from the group consisting of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Furthermore, the processor may include a main processor and a coprocessor. The main processor is a processor for processing data in an awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in a standby state.

[0107] Example 4

[0108] This embodiment provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, they are used to implement the howling detection frequency optimization method described in Example 1.

[0109] Example 5

[0110] The embodiments of the present application may further provide a computer program product, including a computer program, which, when executed by a processor, implements the method for optimizing the frequency point of howling detection as described in Embodiment 1.

[0111] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for optimizing the frequency of howling detection, characterized in that: include: Acquire a test voice signal and convert the test voice signal into a digital signal to obtain a signal to be processed, wherein the test voice signal is used to represent the voice signal collected in real time by the device to be optimized; Based on the signal to be processed, a target spectrum corresponding to the howling detection frequency point is generated, and the howling point in the target spectrum is determined to obtain the howling detection frequency point; Calibrate the howling detection frequency point according to the target spectrum to obtain a calibrated howling frequency point; Using the calibrated howling frequency as the attenuation frequency corresponding to the notch filter in the device to be optimized, howling suppression is achieved, and optimization of the howling detection frequency is completed; Wherein, according to the target spectrum, the howling detection frequency point is calibrated to obtain the calibrated howling frequency point, including: for a certain howling detection frequency point X(f, k), determining the previous frequency point X(f, k+1) and the next frequency point X(f, k-1) of the howling detection frequency point in the target spectrum, wherein X(f, k) is used to represent the frequency corresponding to the frequency point k in the target spectrum, X(f, k+1) is used to represent the frequency corresponding to the frequency point k+1 in the target spectrum, and X(f, k-1) is used to represent the frequency corresponding to the frequency point k-1 in the target spectrum; Obtain the correction coefficient corresponding to the howling detection frequency point based on the previous frequency point X(f, k+1) and the next frequency point X(f, k-1); The howling detection frequency point is calibrated according to the correction coefficient to obtain a calibrated howling frequency point.

2. The method for optimizing the frequency point of howling detection according to claim 1, wherein: Based on the signal to be processed, a target spectrum corresponding to the howling detection frequency point is generated, including: downsampling the signal to be processed according to multiples of 1, 2, 4, and 8 to obtain four initial signals; Perform 512-point FFT processing on the four initial signals to obtain a first spectrum F1, a second spectrum F2, a third spectrum F3, and a fourth spectrum F4; The first spectrum F1, the second spectrum F2, the third spectrum F3 and the fourth spectrum F4 are combined to obtain a target spectrum corresponding to the howling detection frequency point; The peak value in the target spectrum corresponding to the howling detection frequency point is tracked to obtain the howling detection frequency point.

3. The method for optimizing the frequency point of howling detection according to claim 2, wherein: The first spectrum F1, the second spectrum F2, the third spectrum F3 and the fourth spectrum F4 are combined, including: extracting the spectrum in the frequency range of 20 to 2343 Hz from the first spectrum F1 to obtain a first sub-spectrum; Extract the frequency spectrum in the frequency range of 2343 to 4686 Hz from the second spectrum F2 to obtain a second sub-spectrum; Extract the frequency spectrum in the frequency range of 4686 to 9375 Hz from the third spectrum F3 to obtain a third sub-spectrum; Extract the frequency spectrum in the frequency range of 9375 to 20000 Hz from the fourth spectrum F4 to obtain a fourth sub-spectrum; The first sub-spectrum, the second sub-spectrum, the third sub-spectrum, and the fourth sub-spectrum are combined into a spectrum in the frequency range of 20 Hz to 20 kHz to obtain a target spectrum corresponding to the howling detection frequency point.

4. The method for optimizing the frequency point of howling detection according to claim 1, wherein: According to the previous frequency point X(f, k+1) and the next frequency point X(f, k-1), a correction coefficient corresponding to the howling detection frequency point is obtained, including: obtaining the median between the previous frequency point X(f, k+1) and the next frequency point X(f, k-1), and obtaining the first difference MDiff(f, k) between the howling detection frequency point X(f, k) and the median as: MDiff(f, k) = X(f, k)-0.5*(X(f, k+1)+X(f, k-1)) obtaining the second difference XDiff(f, k) between the previous frequency point X(f, k+1) and the next frequency point X(f, k-1) as: Diff(f, k) = X(f, k+1)-X(f, k-1) determining the correction coefficient according to the first difference MDiff(f, k) and the second difference XDiff(f, k).

5. The method for optimizing the frequency point of howling detection according to claim 4, wherein: According to the first difference MDiff(f,k) and the second difference XDiff(f,k), the correction coefficient corr(f,k) is determined as: Among them, real means taking the real part, and conj means taking the conjugate.

6. The method for optimizing the frequency point of howling detection according to claim 5, wherein: According to the correction coefficient, the howling detection frequency is calibrated as follows: where represents the calibration howling frequency, FS represents the sampling frequency of the device to be optimized, and NFFT represents the number of points in FFT processing.

7. A howling detection frequency optimization device, characterized in that: It includes a signal acquisition module, a howling detection module, a first optimization module and a second optimization module; The signal acquisition module is used to acquire a test voice signal and convert the test voice signal into a digital signal to obtain a signal to be processed, wherein the test voice signal is used to represent the voice signal collected in real time by the device to be optimized; The howling detection module is used to generate a target spectrum corresponding to a howling detection frequency point based on the signal to be processed, and determine the howling point in the target spectrum to obtain the howling detection frequency point; The first optimization module is configured to calibrate the howling detection frequency point according to the target spectrum to obtain a calibrated howling frequency point; The second optimization module is configured to use the calibrated howling frequency as the attenuation frequency corresponding to the notch filter in the device to be optimized, thereby achieving howling suppression and completing the optimization of the howling detection frequency; Wherein, according to the target spectrum, the howling detection frequency point is calibrated to obtain the calibrated howling frequency point, including: for a certain howling detection frequency point X(f, k), determining the previous frequency point X(f, k+1) and the next frequency point X(f, k-1) of the howling detection frequency point in the target spectrum, wherein X(f, k) is used to represent the frequency corresponding to the frequency point k in the target spectrum, X(f, k+1) is used to represent the frequency corresponding to the frequency point k+1 in the target spectrum, and X(f, k-1) is used to represent the frequency corresponding to the frequency point k-1 in the target spectrum; Obtain the correction coefficient corresponding to the howling detection frequency point based on the previous frequency point X(f, k+1) and the next frequency point X(f, k-1); The howling detection frequency point is calibrated according to the correction coefficient to obtain a calibrated howling frequency point.

8. A howling detection frequency optimization device, characterized in that: including processor and memory; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the howling detection frequency optimization method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method for optimizing the frequency point of howling detection according to any one of claims 1 to 6.

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