Method and device for determining noise source, and storage medium

Abstract

The application provides a noise source determination method and device and a storage medium. The method comprises: acquiring noise audio signals collected by each microphone in a microphone array; the noise audio signals are emitted by a noise source; performing time-frequency variation on the noise audio signals collected by each microphone to obtain frequency spectrum information of the noise audio signals corresponding to each microphone; acquiring a target frequency, and obtaining position information of the noise source based on the target frequency and the frequency spectrum information of the noise audio signals corresponding to each microphone; performing spatial filtering on the noise audio signals collected by each microphone based on the position information of the noise source to obtain target noise signals in each noise audio signal; and determining the noise source based on the target noise signals and a component frequency spectrum feature library of an electronic device; the noise source is used to represent a target component emitting noise. The method can distinguish multiple noise sources that are close in space, thereby accurately locating a specific component generating noise.

Description

Technical Field

[0001] This application relates to the field of sound source localization technology, and in particular to a method, apparatus and storage medium for determining a noise source. Background Technology

[0002] Electronic devices such as laptops often generate various noises during operation, affecting the user experience. To improve the user experience, identifying the source of the noise is crucial. Traditional noise diagnosis methods mainly rely on manual listening, but manual listening is not only time-consuming and laborious, but also difficult to guarantee accuracy. Summary of the Invention

[0003] This application provides a method, apparatus, and storage medium for determining a noise source, in order to at least solve the above-mentioned technical problems existing in the prior art.

[0004] A first aspect of this application provides a method for determining a noise source, the method comprising: Acquire the noise audio signals collected by each microphone in the microphone array; the noise audio signals are emitted by the noise source. The noise audio signals collected by each microphone are subjected to time-frequency variation to obtain the spectral information of the noise audio signal corresponding to each microphone; The target frequency is obtained, and the location information of the noise source is obtained based on the target frequency and the spectral information of the corresponding noise audio signal of each microphone. Based on the location information of the noise source, spatial filtering is performed on the noise audio signals collected by each microphone to obtain the target noise signal in each noise audio signal; Based on the target noise signal and the component spectral feature library for electronic devices, the noise source is determined; the noise source is used to characterize the target component emitting the noise.

[0005] In one possible implementation, the location information of the noise source includes the direction and distance of the sound source; based on the target frequency and the spectral information of the noise audio signal corresponding to each microphone, the location information of the noise source is obtained, including: Based on the target frequency, the steering vector of the noise source relative to the microphone array is obtained; the steering vector is used to characterize the response information of the noise source when it reaches the microphone array. Based on the spectral information and steering vector of the noise audio signal corresponding to each microphone, the sound source direction of the noise source relative to the microphone array is determined; Obtain the location information of each microphone; Based on the direction of the sound source and the position information of each microphone, the distance of the noise source relative to the microphone array is determined.

[0006] In one possible implementation, the steering vector of the noise source relative to the microphone array is obtained based on the target frequency, including: Obtain the preset reference distance of the noise source relative to the microphone array; Based on the target frequency, preset reference distance, and the position information of each microphone, the vector components of the noise source relative to each microphone are determined; the vector components are used to characterize the response information of the noise source when it reaches each microphone. Based on the vector components of the noise source relative to each microphone, the steering vector of the noise source relative to the microphone array is obtained.

[0007] In one possible implementation, the sound source direction relative to the microphone array is determined based on the spectral information of the noise audio signal corresponding to each microphone and the steering vector, including: Based on the spectral information of the noise audio signal corresponding to each microphone, the covariance matrix for the microphone array is determined; Based on a preset step size, the target angle range is divided to obtain multiple scanning angles; For each scanning angle, the spatial spectrum value at the scanning angle is determined based on the covariance matrix and the steering vector; The scanning angle corresponding to the maximum spatial spectrum value is taken as the sound source direction of the noise source relative to the microphone array.

[0008] In one possible implementation, determining the distance of the noise source relative to the microphone array based on the direction of the sound source and the position information of each microphone includes: Based on the spectral information of the noise audio signal corresponding to each microphone, the phase difference between each microphone is determined; Based on the phase difference between each microphone, the direction of the sound source, and the position information of each microphone, a set of calculation equations for the distance to the sound source is determined; Based on the objective solution algorithm, the system of equations for calculating the distance to the sound source is solved to obtain the distance of the noise source relative to the microphone array.

[0009] In one possible implementation, the noise source is determined based on the target noise signal and a component spectral feature library for the electronic device, including: Obtain the power spectrum of the target noise signal; the power spectrum of the target noise signal includes line spectrum features, which include line spectrum frequencies; Determine the maximum value of the line spectrum frequency in the power spectrum; The noise source is determined based on the maximum value and the spectral characteristics of each component in the component spectral feature library.

[0010] In one embodiment, the power spectrum of the target noise signal further includes harmonic structure features and envelope structure features; Based on the target noise signal and a spectral feature library of components for electronic devices, the noise sources are identified, including: For each component in the component spectral feature library, the correlation between the target noise signal and the spectral features of each component is determined, including the correlation of line spectrum features, harmonic structure, and envelope structure. The weight information corresponding to the correlation of spectral features, harmonic structure, and envelope structure is obtained respectively. Based on the correlation of line spectrum features, harmonic structure, envelope structure, and weight information between the target noise signal and the spectral features of each component, the Euclidean distance between the target noise signal and the spectral features of each component is obtained. The noise source is determined based on the Euclidean distance between the target noise signal and the spectral characteristics of each component.

[0011] A second aspect of this application provides a noise source determination device, the device comprising: The first acquisition unit is used to acquire the noise audio signals collected by each microphone in the microphone array; the noise audio signals are emitted by the noise source. The second acquisition unit is used to perform time-frequency transformation on the noise audio signals collected by each microphone to obtain the spectral information of the corresponding noise audio signals for each microphone. The third acquisition unit is used to acquire the target frequency and, based on the target frequency and the spectral information of the noise audio signal corresponding to each microphone, obtain the location information of the noise source. The fourth acquisition unit is used to perform spatial filtering on the noise audio signals collected by each microphone based on the location information of the noise source, so as to obtain the target noise signal in each noise audio signal. The first determining unit is used to determine the noise source based on the target noise signal and a component spectral feature library for electronic devices; the noise source is used to characterize the target component emitting the noise.

[0012] In one embodiment, the location information of the noise source includes the direction of the sound source and the distance to the sound source; the third acquisition unit is used to obtain the steering vector of the noise source relative to the microphone array based on the target frequency; the steering vector is used to characterize the response information when the noise source reaches the microphone array; the direction of the noise source relative to the microphone array is determined based on the spectral information of the noise audio signal corresponding to each microphone and the steering vector; the location information of each microphone is acquired; and the distance to the sound source of the noise source relative to the microphone array is determined based on the direction of the sound source and the location information of each microphone.

[0013] A third aspect of this application provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the method of this application.

[0014] In this application, noise audio signals collected by each microphone in a microphone array are acquired; these noise audio signals are emitted by a noise source; time-frequency variation is performed on the noise audio signals collected by each microphone to obtain the spectral information of the corresponding noise audio signals for each microphone; a target frequency is acquired, and based on the target frequency and the spectral information of the corresponding noise audio signals for each microphone, the location information of the noise source is obtained; based on the location information of the noise source, spatial filtering is performed on the noise audio signals collected by each microphone to obtain the target noise signal in each noise audio signal; based on the target noise signal and a component spectral feature library for electronic devices, the noise source is determined; the noise source is used to characterize the target component emitting the noise. Compared to related technologies that determine the noise source by manual listening, this application, after determining the location of the noise source based on the noise audio signals collected by the microphone array, further determines the component emitting the noise source by combining it with the component spectral feature library of electronic devices. This can distinguish multiple spatially close noise sources, thereby accurately locating the specific component where the noise occurs and improving the accuracy of noise source determination.

[0015] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0016] The above and other objects, features, and advantages of exemplary embodiments of this application will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. Several embodiments of this application are illustrated in the drawings by way of example and not limitation, in which: In the accompanying drawings, the same or corresponding reference numerals indicate the same or corresponding parts.

[0017] Figure 1 A schematic diagram illustrating the implementation flow of the noise source determination method according to an embodiment of this application is shown; Figure 2 A schematic diagram illustrating the application of the noise source determination method according to an embodiment of this application is shown; Figure 3 A schematic diagram of the composition of the noise source determination device according to an embodiment of this application is shown. Detailed Implementation

[0018] To make the objectives, features, and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] This application provides a method for determining a noise source, which can be applied to electronic devices, such as PCs, servers, etc. Figure 1 , Figure 2 As shown, the method includes: S101: The electronic device acquires the noise audio signals collected by each microphone in the microphone array; the noise audio signals are emitted by the noise source; In this application, the electronic device can be a laptop, desktop computer, etc. Components inside the electronic device, such as fans, solid-state drives, and inductors, typically generate noise that affects the user experience during operation. This embodiment uses a microphone array located outside the electronic device to collect noise audio signals generated by noise sources inside the electronic device. The microphone array includes at least two microphones, preferably four. Specifically, the microphone array can include four triaxial (X, Y, Z axes) microphones arranged in a cross shape, with two microphones symmetrically distributed on the same axis. When collecting noise audio signals through the microphone array, each microphone in the array collects the corresponding noise audio signal; however, due to differences in the position and orientation of each microphone, the sound channels entering the same noise source will also differ from microphone to microphone.

[0020] S102: The electronic device performs time-frequency transformation on the noise audio signals collected by each microphone to obtain the spectrum information of the corresponding noise audio signals for each microphone; In this step, the noise audio signal is a time-domain signal. To facilitate calculation and analysis, the noise audio signals collected by each microphone are subjected to time-frequency transformation to convert the time-domain signal into a frequency-domain signal. Specifically, a Fourier transform is performed on the noise audio signals collected by each microphone to obtain the complex spectrum information of the noise audio signal corresponding to each microphone. The complex spectrum information includes frequency, amplitude, and phase.

[0021] S103: The electronic device acquires the target frequency and obtains the location information of the noise source based on the target frequency and the spectral information of the corresponding noise audio signal of each microphone; In this application, the target frequency is a predetermined signal frequency used to determine the signal data at a specific frequency. The noise source location information includes the direction of the noise source, which can be obtained through the following steps: determining the complex values ​​at the target frequency in the spectrum of the noise audio signal corresponding to each microphone, i.e., the phase and amplitude at the target frequency. Based on the complex values ​​of the noise audio signal corresponding to each microphone at the target frequency, a spatial spectrum scan is performed, such as an MVDR (Minimum Variance Distortionless Response) spatial spectrum scan, to finally determine the noise source location information.

[0022] S104: The electronic device performs spatial filtering on the noise audio signals collected by each microphone based on the location information of the noise source to obtain the target noise signal in each noise audio signal; In this step, considering the numerous reflective surfaces inside the electronic device (such as the motherboard, casing, and heat sink), the sound received by each microphone includes not only the original noise but also irrelevant echoes after multiple reflections. To ensure the accuracy of noise source identification, this embodiment, after determining the noise source's location information, only amplifies the audio signal originating from the direction indicated by the noise source's location information. Specifically, a fixed beamforming method can be used to compensate each microphone's corresponding noise audio signal with a time delay (or phase) corresponding to the direction of arrival, aligning the signal from that direction across all microphone channels. These signals are then directly added together. After alignment and superposition, the signal from the direction indicated by the noise source's location information is coherently superimposed, resulting in increased amplitude. Signals from other directions, due to incomplete phase alignment (i.e., incoherent superposition), have relatively smaller amplitudes. Ultimately, spatial amplification of signals from specific directions is achieved, enabling spatial filtering of the acquired noise audio signals to obtain a relatively clean target noise signal.

[0023] S105: The electronic device determines the noise source based on the target noise signal and a component spectral feature library for the electronic device; the noise source is used to characterize the target component emitting the noise.

[0024] In this embodiment, the power spectrum of the target noise signal is obtained. The power spectrum of the target noise signal can reflect the core characteristics of its waveform (such as line spectrum features, harmonic structure, etc.) and the energy distribution of its waveform. The power spectrum of the target noise signal can be obtained by estimating the power spectral density (PSD) of the target noise signal. For the specific principles and calculation process of PSD estimation, please refer to relevant technologies, which will not be elaborated here. Considering that the components within the electronic device may be closely distributed, in order to further improve the accuracy of the target component, this embodiment compares and matches the features in the power spectrum of the target noise signal with the typical features of each component in the component spectral feature library of the electronic device, thereby accurately locating the target component emitting the noise.

[0025] The component spectrum feature library of electronic devices includes the spectrum features of each component, such as typical frequency bands and triggering conditions. Part of the component spectrum feature library can be found in Table 1. Table 1

[0026] By combining the component spectrum feature library of electronic devices, it is possible to determine which component(s) is emitting noise by comparing typical features in typical frequency bands as shown in Table 1, or by comparing the current usage scenario with the operating conditions triggering conditions as shown in Table 1.

[0027] In the scheme shown in steps S101 to S105, noise audio signals collected by each microphone in the microphone array are acquired; the noise audio signals are emitted by a noise source; time-frequency variation is performed on the noise audio signals collected by each microphone to obtain the spectral information of the corresponding noise audio signals for each microphone; a target frequency is acquired, and the location information of the noise source is obtained based on the target frequency and the spectral information of the corresponding noise audio signals of each microphone; based on the location information of the noise source, spatial filtering is performed on the noise audio signals collected by each microphone to obtain the target noise signal in each noise audio signal; the noise source is determined based on the target noise signal and the component spectral feature library for electronic devices; the noise source is used to characterize the target component emitting the noise. Compared with related technologies that determine the noise source by listening manually, this application determines the location of the noise source based on the noise audio signals collected by the microphone array, and then further determines the component emitting the noise source by combining the component spectral feature library of electronic devices. This can distinguish multiple spatially close noise sources, thereby accurately locating the specific component where the noise occurs and improving the accuracy of noise source determination.

[0028] In some alternative schemes, the location information of the noise source includes the direction and distance of the sound source; based on the target frequency and the spectral information of the corresponding noise audio signal of each microphone, the location information of the noise source is obtained, including: Based on the target frequency, the steering vector of the noise source relative to the microphone array is obtained; the steering vector is used to characterize the response information of the noise source when it reaches the microphone array. Based on the spectral information and steering vector of the noise audio signal corresponding to each microphone, the sound source direction of the noise source relative to the microphone array is determined; Obtain the location information of each microphone; Based on the direction of the sound source and the position information of each microphone, the distance of the noise source relative to the microphone array is determined.

[0029] In this application, combined with Figure 2 As shown, the location information of the noise source also includes the sound source distance. The sound source direction is the angle between the line connecting the direction of noise propagation and the center point of the microphone array and the normal, including the azimuth and elevation angles. The sound source distance is the distance between the specific point emitting noise in the sound source direction and the center point of the microphone array. In this embodiment, the sound source direction can be determined by spatial spectrum scanning based on the steering vector and the spectrum diagram of the corresponding noise audio signal of each microphone, and the steering vector can be calculated based on the target frequency. For the specific process of obtaining the steering vector and the sound source direction, please refer to the detailed description in the relevant sections below, which will not be repeated here.

[0030] The location information of each microphone includes its coordinates, the distance between microphones, etc. The sound source distance can be obtained by constructing and solving a system of equations based on the location information of each microphone and the direction of the sound source. For a detailed explanation of the specific calculation process of the sound source distance, please refer to the relevant sections below; it will not be repeated here. This embodiment, by obtaining the direction (including azimuth and elevation angles) and distance of the noise source, can achieve three-dimensional spatial localization of the noise source, providing more comprehensive location information, which helps to accurately identify the noise source and perform subsequent processing.

[0031] In some alternative approaches, the steering vector of the noise source relative to the microphone array is obtained based on the target frequency, including: Obtain the preset reference distance of the noise source relative to the microphone array; Based on the target frequency, preset reference distance, and the position information of each microphone, the vector components of the noise source relative to each microphone are determined; the vector components are used to characterize the response information of the noise source when it reaches each microphone. Based on the vector components of the noise source relative to each microphone, the steering vector of the noise source relative to the microphone array is obtained.

[0032] In this application, the preset reference distance is a pre-set virtual distance, that is, the assumed distance from the noise source to the microphone array. In this embodiment, multiple azimuth angles are obtained by dividing the angle range from 0° to 360° in 1° increments, and multiple pitch angles are obtained by dividing the angle range from 0° to 90° in 1° increments. The multiple azimuth angles and multiple pitch angles are arbitrarily combined in pairs. Under each pair of azimuth angles and pitch angles, the vector components for each microphone are calculated according to the preset reference distance, that is, the response of the noise source to each microphone under the assumed conditions (assumed direction, assumed distance). The vector components can be calculated by formula (1): Formula (1) in, Let be the vector component for the m-th microphone. This is a preset reference distance. Let be the distance between the noise source and the m-th microphone. It can be calculated based on geometric relationships using r and the position information of each microphone. denoted as ... It is an imaginary unit and has no practical meaning.

[0033] The vector components corresponding to each microphone constitute the guide vector of the noise source relative to the microphone array; that is, the guide vector is a vector that includes the vector components corresponding to each microphone. In this embodiment, a preset reference distance is simulated, and the vector components of each microphone are calculated based on it. Finally, a guide vector containing both geometric information (angle, distance assumptions) and physical information (frequency, sound speed) is constructed. This provides a high-quality data foundation for subsequent spatial spectrum scanning (determining the direction of the sound source), thereby achieving high-precision noise source localization.

[0034] In some alternative schemes, the sound source direction relative to the microphone array is determined based on the spectral information of the noise audio signal corresponding to each microphone and the steering vector, including: Based on the spectral information of the noise audio signal corresponding to each microphone, the covariance matrix for the microphone array is determined; Based on a preset step size, the target angle range is divided to obtain multiple scanning angles; For each scanning angle, the spatial spectrum value at the scanning angle is determined based on the covariance matrix and the steering vector; The scanning angle corresponding to the maximum spatial spectrum value is taken as the sound source direction of the noise source relative to the microphone array.

[0035] In this application, the covariance matrix of the microphone array is calculated as shown in formula (2): Formula (2) in, Let be the covariance matrix. It is a column vector containing 4 elements, representing the complex values ​​(amplitude and phase) of the target frequency in the spectrum of the 4 microphones at the i-th snapshot time. for The conjugate transpose of .

[0036] Similar to the aforementioned description of the steering vector calculation process, multiple azimuth angles are obtained by dividing the angular range from 0° to 360° in 1° increments, and multiple elevation angles are obtained by dividing the angular range from 0° to 90° in 1° increments. Multiple azimuth angles and multiple elevation angles are arbitrarily combined in pairs. Under each pair of azimuth and elevation angles, a spatial spectrum scan is performed according to formula (3) to obtain the spatial spectrum values: Formula (3) in, This represents the spatial spectral value. Let be the inverse of the covariance matrix R. This is the steering vector under the combination of azimuth angle θ and elevation angle φ. for The conjugate transpose of .

[0037] The azimuth and elevation angles of each group can be calculated using the formula (3) above. Afterwards, The azimuth and elevation angles corresponding to the maximum values ​​are used as the sound source direction of the noise source. This is because when the direction is assumed to be correct, spatial spectrum scanning can most effectively preserve the signal and suppress interference, thereby obtaining the maximum output energy (spatial spectrum value). This embodiment accurately and robustly locates the direction of the sound source in three-dimensional space by traversing each set of azimuth and elevation angles and spatial spectrum scanning calculations, laying a key foundation for subsequent distance calculation and component identification.

[0038] In some alternative approaches, the distance of the noise source relative to the microphone array is determined based on the direction of the sound source and the position information of each microphone, including: Based on the spectral information of the noise audio signal corresponding to each microphone, the phase difference between each microphone is determined; Based on the phase difference between each microphone, the direction of the sound source, and the position information of each microphone, a set of calculation equations for the distance to the sound source is determined; Based on the objective solution algorithm, the system of equations for calculating the distance to the sound source is solved to obtain the distance of the noise source relative to the microphone array.

[0039] In this application, considering that under near-field (spherical wave) conditions, the phase difference of signals received by different microphones is not only related to the direction of the noise source, but also has a nonlinear relationship with the distance to the source. Therefore, when the sound source is located in the near field, the wavefront curvature cannot be ignored, and the distance to the sound source can be calculated using the phase difference between two microphones on the same axis. As mentioned above, the spectrum diagram includes phase values, so based on the spectrum diagram of the noise audio signal corresponding to each microphone, the phase difference at the target frequency can be extracted and subtracted to obtain the phase difference between each microphone.

[0040] Specifically, taking a microphone pair located on the X-axis (Mic1 to the left, Mic2 to the right) as an example, let the noise source coordinates be (x, y, z), the element spacing be d, the origin O be located at (0, 0, 0), Mic1 be (-d / 2, 0, 0), and Mic2 be (d / 2, 0, 0). Then the distance from the noise source to Mic1 is... Distance from noise source to Mic2 The phase difference between the signals received by the two microphones It can be expressed by formula (4): Formula (4) in, It is a positive number, and its initial value is usually 0. and Please refer to the explanation of the aforementioned formula for the physical meaning, which will not be repeated here. Substituting r1 and r2 into formula (4) and combining the geometric relationship, we get: Formula (5) in, The distance to the sound source is to be determined. The angle between the direction of the sound source and the horizontal line connecting Mic1 and Mic2 is given.

[0041] Similarly, a system of equations is constructed based on the microphone pairs located on the Y-axis (Mic3 above, Mic4 below) and the microphone pairs on the diagonal: Formula (6) Will Substituting the transformation shown in formula (5) into formula (6), and... , The deformations (the deformations of both can be obtained by referring to formula (5) and combining the geometric relationships of each microphone) are substituted into formula (6), and the Newton-Raphson iterative algorithm is used to solve formula (6) to obtain the final sound source distance r. In this embodiment, the nonlinear relationship between phase difference and distance under near-field spherical wave conditions is used to solve a set of equations, which can effectively suppress the influence of single-pair microphone measurement errors (such as noise interference and phase winding) on ​​the final result, and improve the error resistance and stability of distance measurement.

[0042] In some alternative approaches, the noise source is determined based on the target noise signal and a library of component spectral characteristics for electronic devices, including: Obtain the power spectrum of the target noise signal; the power spectrum of the target noise signal includes line spectrum features, which include line spectrum frequencies; Determine the maximum value of the line spectrum frequency in the power spectrum; The noise source is determined based on the maximum value and the spectral characteristics of each component in the component spectral feature library.

[0043] In this application, the power spectrum of the target noise signal can reflect the waveform characteristics of the target noise signal, including line spectrum characteristics, harmonic structure characteristics, and envelope structure characteristics. Line spectrum characteristics include the frequency values ​​and signal-to-noise ratios of single-frequency components (e.g., fan BPF, inductor howling fundamental frequency); harmonic structure characteristics include the amplitude ratio between the fundamental frequency and its harmonics (e.g., the odd / even harmonic energy ratio of the frequency through which fan blades pass); envelope structure characteristics include two aspects: modulation sidebands and spectral width and kurtosis. Modulation sidebands include whether there are equally spaced sidebands on both sides of the main frequency (e.g., fan bearing failure will cause speed frequency modulation). Spectral width and kurtosis include the concentrated frequency band of broadband noise and the sharpness of the peak values. Combined with... Figure 2As shown, in this embodiment, the target component emitting noise can be determined using a hard threshold screening method. Specifically, the maximum value of the line spectrum frequency is determined. Combining the spectral characteristics of each component in the component spectral feature library, if the maximum value of the line spectrum frequency is <1000Hz, high-frequency components such as VRM inductor howling and SSD howling are directly excluded. The remaining components are the candidate target components. When the number of candidate target components is 1, the candidate target component is the final target component emitting noise. This method can quickly eliminate physically impossible component options, reducing the amount of computation.

[0044] In some alternative schemes, the power spectrum of the target noise signal also includes harmonic structure features and envelope structure features; Based on the target noise signal and a spectral feature library of components for electronic devices, the noise sources are identified, including: For each component in the component spectral feature library, the correlation between the target noise signal and the spectral features of each component is determined, including the correlation of line spectrum features, harmonic structure, and envelope structure. The weight information corresponding to the correlation of spectral features, harmonic structure, and envelope structure is obtained respectively. Based on the correlation of line spectrum features, harmonic structure, envelope structure, and weight information between the target noise signal and the spectral features of each component, the Euclidean distance between the target noise signal and the spectral features of each component is obtained. The noise source is determined based on the Euclidean distance between the target noise signal and the spectral characteristics of each component.

[0045] In this embodiment, combined with Figure 2 As shown, when the number of candidate target components is not 1, the target component can be determined by weighted Euclidean distance. Specifically, the weighted Euclidean distance of each component can be calculated using formula (7): Formula (7) in, This is the weighted Euclidean distance. This represents the correlation of spectral features. This represents the harmonic structure correlation. This represents the correlation of the envelope structure. , , These are the weights corresponding to the three correlations. The values ​​of the weights can be customized according to actual needs. Spectral feature correlation includes the line spectrum position difference, which can be obtained by calculating the relative or absolute error between the target noise signal and the typical waveforms of each component on the line spectrum features. Harmonic structure correlation can be obtained by calculating the cosine similarity or Euclidean distance between the target noise signal and the typical waveforms of each component on the harmonic features. Envelope structure correlation can be obtained by calculating the cosine similarity or Pearson correlation coefficient between the target noise signal and the typical waveforms of each component on the envelope structure features. (Details omitted). The component with the smallest S value (i.e., closest distance, highest similarity) is selected as the target component. This embodiment, by performing spectral feature matching on the target noise signal and the typical waveforms of each component, can accurately determine the target component emitting the noise, improving the accuracy of noise source identification.

[0046] This application also provides a device for determining a noise source, such as... Figure 3 As shown, the device includes: The first acquisition unit 301 is used to acquire the noise audio signals collected by each microphone in the microphone array; the noise audio signals are emitted by the noise source. The second acquisition unit 302 is used to perform time-frequency transformation on the noise audio signals collected by each microphone to obtain the spectrum information of the corresponding noise audio signals for each microphone. The third acquisition unit 303 is used to acquire the target frequency and obtain the location information of the noise source based on the target frequency and the spectral information of the noise audio signal corresponding to each microphone. The fourth acquisition unit 304 is used to perform spatial filtering on the noise audio signals collected by each microphone based on the location information of the noise source, so as to obtain the target noise signal in each noise audio signal. The first determining unit 305 is used to determine the noise source based on the target noise signal and the component spectral feature library for electronic devices; the noise source is used to characterize the target component that emits the noise.

[0047] In some alternative schemes, the location information of the noise source includes the direction of the sound source and the distance to the sound source; the third acquisition unit 303 is used to obtain the steering vector of the noise source relative to the microphone array based on the target frequency; the steering vector is used to characterize the response information when the noise source arrives at the microphone array; the direction of the noise source relative to the microphone array is determined based on the spectral information of the noise audio signal corresponding to each microphone and the steering vector; the location information of each microphone is acquired; and the distance of the noise source relative to the microphone array is determined based on the direction of the sound source and the location information of each microphone.

[0048] In some alternative solutions, the third acquisition unit 303 is used to acquire the preset reference distance of the noise source relative to the microphone array; determine the vector components of the noise source relative to each microphone based on the target frequency, the preset reference distance and the position information of each microphone; the vector components are used to characterize the response information when the noise source reaches each microphone; and obtain the steering vector of the noise source relative to the microphone array based on the vector components of the noise source relative to each microphone.

[0049] In some alternative schemes, the third acquisition unit 303 is used to determine the covariance matrix for the microphone array based on the spectral information of the noise audio signal corresponding to each microphone; divide the target angle range based on a preset step size to obtain multiple scanning angles; for each scanning angle, determine the spatial spectrum value under the scanning angle based on the covariance matrix and the steering vector; and take the scanning angle corresponding to the maximum spatial spectrum value as the sound source direction of the noise source relative to the microphone array.

[0050] In some alternative schemes, the third acquisition unit 303 is used to determine the phase difference between each microphone based on the spectral information of the noise audio signal corresponding to each microphone; determine a set of calculation equations for the sound source distance based on the phase difference between each microphone, the direction of the sound source and the position information of each microphone; and solve the set of calculation equations for the sound source distance based on the target solving algorithm to obtain the sound source distance of the noise source relative to the microphone array.

[0051] In some alternative schemes, the first determining unit 305 is used to acquire the power spectrum of the target noise signal; the power spectrum of the target noise signal includes line spectrum features, and the line spectrum features include line spectrum frequencies; determine the maximum value of the line spectrum frequencies in the power spectrum; and determine the noise source based on the maximum value and the spectral features of each component in the component spectral feature library.

[0052] In some optional schemes, the power spectrum of the target noise signal also includes harmonic structure features and envelope structure features; the first determining unit 305 is used to determine the line spectrum feature correlation, harmonic structure correlation, and envelope structure correlation between the target noise signal and the spectral features of each component in the component spectral feature library; to obtain the weight information corresponding to the spectral feature correlation, harmonic structure correlation, and envelope structure correlation; to obtain the Euclidean distance between the target noise signal and the spectral features of each component based on the line spectrum feature correlation, harmonic structure correlation, envelope structure correlation, and weight information; and to determine the noise source based on the Euclidean distance between the target noise signal and the spectral features of each component.

[0053] It should be noted that the noise source determination device in this application embodiment solves the problem in a similar way to the aforementioned noise source determination method. Therefore, the implementation process, implementation principle, and beneficial effects of the noise source determination device can be found in the description of the implementation process, implementation principle, and beneficial effects of the aforementioned method. Repeated descriptions will not be repeated.

[0054] According to embodiments of this application, this application also provides a readable storage medium.

[0055] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transferring data and instructions to the storage system, the at least one input device, and the at least one output device.

[0056] The program code used to implement the methods of this application may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0057] In the context of this application, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0058] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0059] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0060] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0061] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this application can be achieved, and this is not limited herein.

[0062] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "a plurality of" means two or more, unless otherwise explicitly specified.

[0063] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for determining a noise source, characterized in that, The method includes: Acquire the noise audio signals collected by each microphone in the microphone array; the noise audio signals are emitted by a noise source; The noise audio signals collected by each microphone are subjected to time-frequency variation to obtain the spectral information of the noise audio signal corresponding to each microphone; Obtain the target frequency, and based on the target frequency and the spectral information of the noise audio signal corresponding to each microphone, obtain the location information of the noise source; Based on the location information of the noise source, spatial filtering is performed on the noise audio signals collected by each microphone to obtain the target noise signal in each noise audio signal; Based on the target noise signal and the component spectral feature library for electronic devices, the noise source is determined; the noise source is used to characterize the target component emitting the noise.

2. The method for determining a noise source according to claim 1, characterized in that, The location information of the noise source includes the direction and distance of the sound source; obtaining the location information of the noise source based on the target frequency and the spectral information of the corresponding noise audio signals of each microphone includes: Based on the target frequency, a steering vector of the noise source toward the microphone array is obtained; the steering vector is used to characterize the response information of the noise source when it reaches the microphone array. Based on the spectral information of the noise audio signal corresponding to each microphone and the steering vector, the sound source direction of the noise source relative to the microphone array is determined; Obtain the location information of each microphone; Based on the direction of the sound source and the position information of each microphone, the distance of the noise source relative to the microphone array is determined.

3. The method for determining a noise source according to claim 2, characterized in that, The step of obtaining the steering vector of the noise source relative to the microphone array based on the target frequency includes: Obtain the preset reference distance of the noise source relative to the microphone array; Based on the target frequency, the preset reference distance, and the position information of each microphone, the vector components of the noise source relative to each microphone are determined; the vector components are used to characterize the response information of the noise source when it reaches each microphone. Based on the vector components of the noise source relative to each microphone, the steering vector of the noise source relative to the microphone array is obtained.

4. The method for determining a noise source according to claim 2 or 3, characterized in that, Determining the sound source direction relative to the microphone array based on the spectral information of the noise audio signal corresponding to each microphone and the steering vector includes: Based on the spectral information of the noise audio signal corresponding to each microphone, the covariance matrix for the microphone array is determined; Based on a preset step size, the target angle range is divided to obtain multiple scanning angles; For each scanning angle, the spatial spectrum value at that scanning angle is determined based on the covariance matrix and the steering vector; The scanning angle corresponding to the maximum spatial spectrum value is taken as the sound source direction of the noise source relative to the microphone array.

5. The method for determining a noise source according to claim 2 or 3, characterized in that, Determining the distance of the noise source relative to the microphone array based on the direction of the sound source and the position information of each microphone includes: Based on the spectral information of the noise audio signal corresponding to each microphone, the phase difference between each microphone is determined; Based on the phase difference between each microphone, the direction of the sound source, and the position information of each microphone, a set of calculation equations for the distance to the sound source is determined; Based on the objective solution algorithm, the set of equations for calculating the distance to the sound source is solved to obtain the distance of the noise source relative to the microphone array.

6. The method for determining a noise source according to claim 1, characterized in that, The step of determining the noise source based on the target noise signal and a component spectral feature library for electronic devices includes: Obtain the power spectrum of the target noise signal; the power spectrum of the target noise signal includes line spectrum features, and the line spectrum features include line spectrum frequencies; Determine the maximum value of the line spectral frequencies in the power spectrum; Based on the maximum value and the spectral characteristics of each component in the component spectral feature library, the noise source is determined.

7. The method for determining a noise source according to claim 6, characterized in that, The power spectrum of the target noise signal also includes harmonic structure features and envelope structure features; The step of determining the noise source based on the target noise signal and a component spectral feature library for electronic devices includes: For each component in the component spectral feature library, the correlation between the target noise signal and the spectral features of each component is determined, including the correlation of line spectrum features, harmonic structure, and envelope structure. The weight information corresponding to the correlation of spectral features, harmonic structure, and envelope structure is obtained respectively. Based on the correlation of line spectrum features, harmonic structure, envelope structure, and weight information between the target noise signal and the spectral features of each component, the Euclidean distance between the target noise signal and the spectral features of each component is obtained. The noise source is determined based on the Euclidean distance between the target noise signal and the spectral characteristics of each component.

8. A noise source identification device, characterized in that, The device includes: The first acquisition unit is used to acquire the noise audio signals collected by each microphone in the microphone array; the noise audio signals are emitted by the noise source. The second acquisition unit is used to perform time-frequency transformation on the noise audio signals collected by each microphone to obtain the spectral information of the corresponding noise audio signals for each microphone. The third acquisition unit is used to acquire the target frequency and, based on the target frequency and the spectral information of the noise audio signal corresponding to each microphone, obtain the location information of the noise source. The fourth acquisition unit is used to perform spatial filtering on the noise audio signals collected by each microphone based on the location information of the noise source, so as to obtain the target noise signal in each noise audio signal; The first determining unit is used to determine the noise source based on the target noise signal and a component spectral feature library for electronic devices; the noise source is used to characterize the target component emitting the noise.

9. The noise source determination device according to claim 8, characterized in that, The location information of the noise source includes the direction of the sound source and the distance to the sound source; the third acquisition unit is used to obtain the steering vector of the noise source relative to the microphone array based on the target frequency; the steering vector is used to characterize the response information of the noise source when it reaches the microphone array; Based on the spectral information of the noise audio signal corresponding to each microphone and the steering vector, the sound source direction of the noise source relative to the microphone array is determined; and the position information of each microphone is obtained. Based on the direction of the sound source and the position information of each microphone, the distance of the noise source relative to the microphone array is determined.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-7.

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