Signal processing method, apparatus, device, system, storage medium and program product

Abstract

The application provides a signal processing method, device, equipment, system, storage medium and program product. The input signal is adjusted based on an audible signal output by the input signal until the difference between the audible signal output and the expected signal is within a preset difference range. Then, the input signal corresponding to the audible signal within the preset range is input to the parametric array loudspeaker as the signal, which is less affected by environmental noise and improves the distortion compensation effect.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of sound processing, and in particular to a signal processing method, device, equipment, system, storage medium and program product. BACKGROUND

[0002] Parametric array loudspeakers use ultrasonic waves as carriers, and modulate audible sound signals on the sidebands of the ultrasonic carrier waves. Due to the nonlinear propagation effect of air, the ultrasonic waves will continuously self-demodulate audible sound signals in the propagation process, and due to the high directivity of the ultrasonic waves, the audible sound signals will be accurately transmitted to the target position.

[0003] The self-demodulation process is nonlinear, and is affected by nonlinear factors such as temperature, humidity, pressure, scattering, and heat propagation loss of the air medium. There is a relatively serious distortion between the input signal of the parametric array loudspeaker and the output audible sound signal, which affects the sound quality of the parametric array loudspeaker.

[0004] How to compensate for the distortion between the input signal of the parametric array loudspeaker and the output audible sound signal is a relatively important problem. SUMMARY

[0005] The present application provides a signal processing method, device, equipment, system, storage medium and program product to solve the problem of signal distortion.

[0006] In a first aspect, the present application provides a signal processing method, which comprises:

[0007] Adjusting the input signal based on the output audible sound signal until the difference between the output audible sound signal and the expected signal is within a preset range;

[0008] Taking the input signal corresponding to the audible sound signal within the preset range from the difference between the expected signal as the signal input to the parametric array loudspeaker.

[0009] In some embodiments, before adjusting the input signal based on the output audible sound signal, the method further comprises:

[0010] Processing the input signal based on a nonlinear filtering model to output an audible sound signal.

[0011] In some embodiments, the input signal and the expected signal each comprise a plurality of sampling points, and the plurality of sampling points of the input signal correspond one-to-one to the plurality of sampling points of the expected signal.

[0012] In some embodiments, the difference between the output audible sound signal and the expected signal within the preset range comprises:

[0013] The difference between the amplitude of at least some sampling points of the output audible signal and the amplitude of corresponding sampling points in the expected signal is within a preset range.

[0014] In some embodiments, the adjusting the input signal until the difference between the output audible signal and the expected signal is within a preset range comprises:

[0015] adjusting the input signal by an adaptive filter, outputting an audible signal based on the adjusted input signal until the difference between the output audible signal and the expected signal is within a preset range.

[0016] In some embodiments, the adaptive filter comprises a normalized least mean square adaptive filter.

[0017] In some embodiments, when the difference is outside the preset range, adjusting the input signal by the normalized least mean square adaptive filter comprises:

[0018] when the difference is less than a minimum value of the preset range, decreasing a weight vector coefficient of the normalized least mean square adaptive filter to decrease the input signal;

[0019] when the difference is greater than a maximum value of the preset range, increasing the weight vector coefficient of the normalized least mean square adaptive filter to increase the input signal.

[0020] In some embodiments, after the output audible signal and the expected signal are within the preset range, the method further comprises:

[0021] modulating the input signal corresponding to the audible signal within the preset range of the expected signal to embed the modulated signal into a high-frequency carrier for high-directivity acoustic wave output.

[0022] In some embodiments, the establishing of the nonlinear filter model comprises:

[0023] identifying a first-order time-domain kernel and a second-order time-domain kernel;

[0024] correcting a third-order time-domain kernel using the first-order time-domain kernel and the second-order time-domain kernel;

[0025] constructing the nonlinear filter model using the first-order time-domain kernel, the second-order time-domain kernel, and the corrected third-order time-domain kernel.

[0026] In some embodiments, the identifying the first-order time-domain kernel and the second-order time-domain kernel comprises:

[0027] identifying a first-order frequency-domain kernel and a second-order frequency-domain kernel using a two-tone method;

[0028] The first-order time domain kernel and the second-order time domain kernel are obtained through inverse Fourier transform based on the first-order frequency domain kernel and the second-order frequency domain kernel.

[0029] In some embodiments, the correcting the third-order time domain kernel based on the first-order time domain kernel and the second-order time domain kernel comprises:

[0030] The first-order output signal is calculated based on the first-order time domain kernel, and the second-order output signal is calculated based on the second-order time domain kernel.

[0031] The third-order expected signal is obtained by subtracting the first-order output signal and the second-order output signal from the expected signal.

[0032] The third-order time domain kernel is corrected based on the third-order expected signal until the difference between the third-order output signal output based on the corrected third-order time domain kernel and the third-order expected signal is within a target range.

[0033] In some embodiments, the correcting the third-order time domain kernel based on the third-order expected signal comprises:

[0034] The third-order time domain kernel is corrected based on the third-order expected signal through a normalized least mean square algorithm.

[0035] In some embodiments, the input signal is processed based on the nonlinear filter model to output an audible signal, comprising:

[0036] The audible signal is obtained and output by adding the first-order output signal, the second-order output signal, and the third-order output signal whose difference with the third-order expected signal is within the target range.

[0037] In a second aspect, the present application provides a signal processing device, the device comprising:

[0038] The adjusting module is configured to adjust the input signal based on the output audible signal until the difference between the output audible signal and the expected signal is within a preset range.

[0039] The output module is configured to output the input signal corresponding to the audible signal whose difference with the expected signal is within the preset range as a signal input to the parametric array loudspeaker.

[0040] In a third aspect, the present application provides a signal processing device, comprising a memory and a processor.

[0041] The memory is configured to store instructions, and the processor is configured to call the instructions in the memory to execute the signal processing method in the first aspect and any possible design of the first aspect.

[0042] In a fourth aspect, the present application provides a signal processing system, comprising the signal processing device and the parametric array loudspeaker.

[0043] The signal processing device inputs, as a signal input to the parametric array loudspeaker, an input signal corresponding to an audible sound signal having a difference from the expected signal within the preset range.

[0044] In a fifth aspect, a computer readable storage medium is provided, and the computer readable storage medium stores computer instructions. When at least one processor of a signal processing device executes the computer instructions, the signal processing device executes the signal processing method in the first aspect and any possible design of the first aspect.

[0045] In a sixth aspect, a computer program product is provided, and the computer program product includes computer instructions. When at least one processor of a signal processing device executes the computer instructions, the signal processing device executes the signal processing method in the first aspect and any possible design of the first aspect.

[0046] The signal processing method, device, system, storage medium and program product provided in the present application adjust the input signal based on the audible sound signal output by the input signal until the audible sound signal output has a difference from the expected signal within the preset range, and input, as a signal input to the parametric array loudspeaker, an input signal corresponding to an audible sound signal having a difference from the expected signal within the preset range, which is less affected by environmental noise and improves the distortion compensation effect. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort.

[0048] Figure 1 The flow chart of the signal processing method provided in an embodiment of the present application;

[0049] Figure 2 The schematic diagram of adjusting the input signal based on the adaptive filter provided in an embodiment of the present application;

[0050] Figure 3 The output signal comparison diagram provided in an embodiment of the present application;

[0051] Figure 4 The flow chart of the signal processing method provided in another embodiment of the present application;

[0052] Figure 5 The construction schematic diagram of the nonlinear filter model provided in an embodiment of the present application;

[0053] Figure 6 Output signal comparison chart provided for another embodiment of the present application;

[0054] Figure 7 Structural schematic diagram of a signal processing device provided for an embodiment of the present application;

[0055] Figure 8 Hardware structural schematic diagram of a signal processing device provided for an embodiment of the present application. DETAILED DESCRIPTION

[0056] In order to make the objectives, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described clearly and completely below with reference to the drawings in the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0057] Sound is one of the most important media for people to convey information, and the environment of daily life is full of various sounds. With the significant improvement of economic and cultural level, people's requirements for the quality of life and working environment are getting higher and higher, and noise control and sound information protection are getting more and more attention.

[0058] In different situations, the demand for sound is different, for example, a library needs a quiet sound environment, and a performance venue needs a clear sound environment. Even the same sound, different audiences will have different needs, for example, for outdoor voice prompts, hearing-impaired people need higher loudness than normal hearing people, and again, the sound of a vehicle horn needs to be clearly transmitted to the target group without disturbing others.

[0059] Therefore, the traditional loudspeaker that can only emit omnidirectional sound waves cannot meet people's needs, and the technology of constructing a local sound field is becoming more and more important.

[0060] At present, directional sound technology can provide different audio information to different users, and with the help of this technology, personalized sound transmission can be realized, and the sound environment can be designed more freely.

[0061] For example, parametric array loudspeakers can emit low-frequency sound in a compact structure size. It uses ultrasonic waves as a carrier and modulates audible sound signals on the sideband of the ultrasonic carrier. Due to the nonlinear propagation effect of air, the ultrasonic wave will continuously self-demodulate the audible sound signal during transmission, and due to the high directivity of the ultrasonic wave, the audible sound signal will be accurately transmitted to the target position.

[0062] The parametric array loudspeaker can also adjust the arrangement of the ultrasonic transducer array according to the needs of different applications to obtain different sound beam shapes, has high flexibility, and therefore has very rich application occasions, various functions that can be realized, and broad application prospects and market demand and value.

[0063] Since the parametric array loudspeaker utilizes ultrasonic signals to self-demodulate in air to generate audible sound with high directivity, the self-demodulation process is nonlinear, is affected by nonlinear factors such as air medium temperature and humidity, pressure, scattering, and heat propagation loss, and there is relatively serious distortion between the input signal and the output audible sound of the parametric array loudspeaker, which affects the sound quality of the parametric array loudspeaker.

[0064] In some embodiments, an approximate solution of the audible sound signal output by the parametric array loudspeaker can be derived based on the Berktay equation, and then the input signal is pre-compensated based on the distortion between the approximate solution and the input signal to reduce the distortion between the input signal and the audible sound signal of the parametric array loudspeaker. However, the Berktay equation is easily affected by environmental noise, resulting in poor compensation effect on distortion.

[0065] In other embodiments, a neural network model of an existing acoustic parametric array system is trained using a large amount of data obtained by testing the acoustic parametric array system, and an inverse model thereof is trained through back propagation. The identified inverse model is connected to the front of the acoustic parametric array system to realize a "pseudo-linear system". An auxiliary adaptive PID (Proportional Integral Derivative) controller is added to realize pre-processing of the signal distortion of the system. However, the training of the neural network requires a large amount of data, which takes a lot of time, and when the data is insufficient, the neural network cannot work, and the system contains a PID controller based on a BP (back propagation) network, and the number of parameters in the BP network is large, and a large number of threshold values and weight values need to be updated each time, which causes the convergence speed to be too slow. Therefore, the present application proposes a signal processing method, which continuously adjusts the input signal based on the audible sound signal output by the input signal, so that the output audible sound signal is closer to the expected signal, effectively compensates for the distortion between the input signal and the audible sound signal of the parametric array loudspeaker, is less affected by environmental noise, and improves the compensation effect. In addition, the number of measurements is less than that of the neural network, the structure is simpler, and the calculation amount is smaller.

[0066] The technical solutions of the present application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0067] An embodiment of the present application provides a scenario of a signal processing method. A signal processing device continuously adjusts an input signal based on an audible sound signal output by the input signal until the audible sound signal output by the input signal is within a preset range of a difference with a desired signal. The signal processing device inputs the input signal corresponding to the audible sound signal within the preset range of the difference with the desired signal to a parametric array loudspeaker. The parametric array loudspeaker outputs an audible sound signal close to the desired signal after receiving the input signal.

[0068] Figure 1 A flowchart of a signal processing method provided by an embodiment of the present application is shown. As shown in the figure, taking a signal processing device as an execution subject, the method of the embodiment can include the following steps. Figure 1

[0069] S101, adjusting the input signal based on an audible sound signal output by the input signal.

[0070] It should be noted that in the process of self-demodulating the audible sound, the parametric array loudspeaker is affected by nonlinear factors such as temperature and humidity, pressure, heat dissipation, heat propagation loss, and the like of the air medium, and the self-demodulated audible sound is distorted, that is, there is a difference between the input signal and the output signal of the parametric array loudspeaker.

[0071] In the embodiment of the present application, when the difference between the desired signal and the audible sound signal is outside the preset range, the input signal is adjusted so that the desired signal can be output based on the adjusted input signal. It can be understood that when the error between the audible sound signal output based on the input signal and the desired signal is large, the input signal is adjusted.

[0072] For example, the desired signal can be the input signal, or a signal close to the input signal. For example, the difference between the desired signal and the input signal is within a preset range, specifically, the desired signal and the input signal each include a plurality of sampling points, the plurality of sampling points of the desired signal correspond one-to-one to the plurality of sampling points of the input signal, and the difference between the first preset number of sampling points of the desired signal and the corresponding sampling points of the input signal can be within the preset range, and the preset range and the first preset number can be determined according to actual conditions.

[0073] For example, the input signal and the desired signal have a plurality of sampling points, and the plurality of sampling points of the input signal correspond one-to-one to the plurality of sampling points of the desired signal, and correspondingly, the audible sound signal also includes a plurality of sampling points, and the plurality of sampling points of the audible sound signal correspond one-to-one to the plurality of sampling points of the desired signal, so that the audible sound signal and the desired signal can be compared.

[0074] ​In some examples, the input signal can be adjusted until the amplitude of at least some sample points of the output audible signal is within a preset range of the amplitude of the corresponding sample points in the desired signal, so that the audible signal is close to the desired signal.

[0075] Correspondingly, the input signal is adjusted when the amplitude of at least some sample points of the output audible signal is outside the preset range of the amplitude of the corresponding sample points in the desired signal. For example, the input signal is adjusted when the difference between the amplitude of the second preset number of sample points of the desired signal and the amplitude of the corresponding sample points of the audible signal is outside the preset range. The second preset number can be determined according to actual conditions.

[0076] In some examples, when the difference between the desired signal and the audible signal simulated based on the input signal is outside the preset range, the input signal can be adjusted by an adaptive filter, and then the audible signal can be output based on the adjusted input signal until the difference between the output audible signal and the desired signal is within the preset range. The adaptive filter is a filter that can automatically adjust filter parameters, and can compensate for distortion between the desired signal and the audible signal in real time, so as to enable the audible signal to be close to the desired signal.

[0077] As an implementation manner, the adaptive filter is a NLMS (Normalization least-mean-square) adaptive filter, which has strong adaptability, high real-time performance, fast convergence speed and small steady-state error, so that the audible signal is closer to the desired signal.

[0078] Specifically, the weight vector coefficient of the NLMS adaptive filter is:

[0079] ω(n)=[ω1,ω2,…,ω M ] T (1)

[0080] wherein, M is the filter length, that is, the order of the filter;

[0081] The input vector at the n time is:

[0082] x(n)=[x(n-0),…,x(n-M+1)] T (2)

[0083] The output of the adaptive filter at the n time is:

[0084] y(n)=x(n) T ω(n) (3)

[0085] The error signal is:

[0086] e(n) = VF(y(n)) - E(n) (4)

[0087] wherein VF(·) represents a nonlinear filter model, VF(y(n)) represents an audible sound signal output based on the input signal, and E(n) represents an expected signal;

[0088] The formula for updating the filter weight vector coefficient is:

[0089]

[0090] wherein μ is an adaptive filter step size, and δ is a number less than 1, generally taken as 0.0001, to prevent the inner product of the input data vector x(n) from being too small to cause system mutation or oscillation.

[0091] Therefore, the weight vector coefficient ω M The input signal y(n) is adjusted. Then, the error signal e(n) between the expected signal E(n) and the audible sound signal E(n)' corresponding to the adjusted input signal is calculated until the error signal e(n) is within the preset range.

[0092] For example, when the difference is less than the minimum value of the preset range, the output audible sound signal is greater than the expected signal, the weight vector coefficient of the normalized least mean square adaptive filter can be reduced to reduce the input signal, so that the audible sound signal output based on the adjusted input signal is close to the expected signal. Wherein the output audible sound signal greater than the expected signal can include the difference between the amplitude of the second preset number of sample points in the output audible sound signal and the amplitude of the corresponding sample points in the expected signal is outside the preset range.

[0093] When the difference is greater than the maximum value of the preset range, the expected signal is greater than the output audible sound signal, the weight vector coefficient of the normalized least mean square adaptive filter can be increased to increase the input signal, so that the audible sound signal output based on the adjusted input signal is close to the expected signal. Wherein the expected signal greater than the output audible sound signal can include the difference between the amplitude of the second preset number of sample points in the expected signal and the amplitude of the corresponding sample points in the simulated audible sound signal is outside the preset range.

[0094] In other implementations, the adaptive filter can also include an LMS (Least-mean-square) adaptive filter and other adaptive filters.

[0095] S102, until the difference between the output audible sound signal and the expected signal is within the preset range.

[0096] It should be noted that step S101 can be performed in a loop until the difference between the output audible sound signal and the expected signal is within the preset range.

[0097] In some embodiments, after the audible sound signal corresponding to the input signal whose difference with the expected signal is within the preset range is modulated to embed the modulated signal into a high-frequency carrier before entering the parametric array loudspeaker, the modulated signal is output with high directivity, so that the parametric array loudspeaker can accurately control the propagation direction of sound and reduce the diffusion and interference of sound. For example, ultrasonic waves (usually at a frequency above 20 kHz) can be used as carrier signals, and audio signals (usually in the frequency range of 20 Hz to 20 kHz) can be embedded into high-frequency carriers through modulation.

[0098] For example, the input signal can be modulated by amplitude modulation (AM), double sideband modulation (DSB), single sideband modulation (SSB), etc.

[0099] S103, the input signal corresponding to the audible sound signal whose difference with the expected signal is within the preset range is input to the parametric array loudspeaker as a signal.

[0100] Correspondingly, the reference loudspeaker can output an audible sound signal close to the expected signal.

[0101] The signal processing method provided by the embodiments of the present application adjusts the input signal based on the audible sound signal output by the input signal, and continuously adjusts until the audible sound signal output based on the adjusted input signal is within the preset range of the expected signal. The input signal corresponding to the audible sound signal within the preset range of the expected signal is input to the parametric loudspeaker as a signal, thereby improving the compensation effect.

[0102] An embodiment of the present application provides a signal processing method, before adjusting the input signal based on the audible sound signal output by the input signal, the method of the embodiment can include the following steps:

[0103] S201, processing the input signal using a nonlinear filtering model to output an audible sound signal.

[0104] In this embodiment, the nonlinear filtering model can process the distortion of the input signal within at least three orders to output the audible sound signal corresponding to the input signal. For example, the audible sound signal simulated by the nonlinear filtering module is the audible sound signal output by the parametric array loudspeaker based on the input signal.

[0105] In some embodiments, considering that the third order and above distortion components of the parametric array loudspeaker are small, only the first three orders can be considered. Specifically, the nonlinear filter is constructed based on a first order time domain kernel, a second order time domain kernel and a third order time domain kernel, the first order time domain kernel representing a linear output component of the parametric array loudspeaker, the second order time domain kernel representing a second harmonic distortion case, and the third order time domain kernel representing a third harmonic distortion case.

[0106] Correspondingly, the nonlinear filtering model can simulate the distortion of the input signal within the third order to simulate the audible sound signal corresponding to the input signal, and improve the accuracy of the simulated audible sound signal.

[0107] Also, in the parametric array loudspeaker, the near field and the third order nonlinear effect are closely related, and the near field region is the region where the nonlinear effect is the strongest, so the third order nonlinear effect is stronger in the near field region, and the generated audible sound wave is more obvious. Therefore, the nonlinear filter can simulate the distortion case of the near field to simulate the audible sound signal closer to the output signal of the parametric array loudspeaker. Here, the output signal of the parametric array loudspeaker is the audible sound signal output based on the input signal, which has distortion compared to the input signal.

[0108] Therefore, as shown in Figure 2 the weight vector coefficient ω M The input signal y(n) is adjusted, and the nonlinear filtering model outputs the corresponding audible sound signal E(n)' based on the adjusted input signal. Then, the error signal e(n) between the expected signal E(n) and the audible sound signal E(n)' corresponding to the adjusted input signal is calculated until the error signal e(n) is within the preset range.

[0109] In the above embodiments, the nonlinear filtering model can approximate the parametric array loudspeaker, and after the nonlinear filtering model simulates the output of the parametric array loudspeaker, the input signal is dynamically adjusted by the adaptive filter to approach the expected signal.

[0110] For example, when the difference is less than the minimum value of the preset range, the simulated audible sound signal is greater than the expected signal, and the weight vector coefficient of the normalized least mean square adaptive filter can be reduced to reduce the input signal, so that the nonlinear filter simulates the audible sound signal based on the adjusted input signal to approach the expected signal. Wherein, the simulated audible sound signal greater than the expected signal can include that the difference between the second preset number of sample values in the simulated audible sound signal and the corresponding sample values in the expected signal is outside the preset range.

[0111] When the difference is greater than a maximum value of the preset range, the expected signal is greater than the simulated audible sound signal, the weight vector coefficient of the normalized least mean square adaptive filter can be increased to increase the input signal, so that the audible sound signal simulated by the nonlinear filter based on the adjusted input signal is close to the expected signal. Wherein, the expected signal being greater than the simulated audible sound signal can include the difference between the second preset number of sample values in the expected signal and the corresponding sample values in the simulated audible sound signal being outside the preset range.

[0112] In order to further verify the scheme of the present application, the audible sound signal simulated by the Berktay model and the audible sound signal simulated by the nonlinear filter model are compared, as shown in Figure 3 Figure 3 The corresponding audible sound signal frequency domain graph when the frequency is 1kHz is shown, and it can be known from Figure 3 that compared with the output signal of the Berktay model, the audible sound signal simulated by the nonlinear filter model is closer to the expected signal.

[0113] The signal processing method provided by the embodiment uses the nonlinear filter model to process the input signal, outputs the audible sound signal, and adjusts the input signal based on the audible sound signal. Through continuous adjustment, the difference between the audible sound signal output based on the adjusted input signal and the expected signal is within the preset range, and the input signal corresponding to the audible sound signal within the preset difference range is taken as the signal input to the parametric loudspeaker, thereby improving the compensation effect.

[0114] Figure 4 The signal processing method provided by another embodiment of the present application is shown in Figure 4 The signal processing method provided by the embodiment of the present application includes:

[0115] S301, a first-order time domain kernel and a second-order time domain kernel are identified.

[0116] It should be noted that if a nonlinear system satisfies four conditions of limited input energy, non-time-varying, continuity and causality, the input-output relationship of the nonlinear system can be represented by Volterra series. For a discrete system, the input-output equation of the system can be given in the form of polynomial series, and the Volterra series model is represented as follows:

[0117]

[0118] Wherein, x(n) is the input signal of the system, y(n) is the output signal of the system, the discrete Volterra series model is also called a nonlinear filter model, h p is called a p-order time domain kernel, M p is the memory length of the p-order time domain kernel. ​

[0119] Since the parametric array loudspeaker has very small distortion components of order three or higher, only the first three time-domain kernels can be considered, and their discrete Fourier transforms are taken to obtain:

[0120]

[0121] where X(·) is the Fourier transform of the input signal, Y(·) is the Fourier transform of the output signal, and H p (·) is the pth-order frequency-domain kernel.

[0122] The relationship between the time-domain kernel and the frequency-domain kernel is:

[0123]

[0124] As can be seen from the above formula, the p-dimensional Fourier transform of the pth-order time-domain kernel can obtain the corresponding frequency-domain kernel.

[0125] In some embodiments, the parameter identification required by the nonlinear filtering model can use an adaptive iterative algorithm such as NLMS. Considering that the NLMS identification accuracy is greatly affected by the signal-to-noise ratio of the identification signal, and the two-tone method is less affected by the signal-to-noise ratio of the identification signal, the identified kernel function will be more accurate. Therefore, the two-tone method is used to identify the first-order time-domain kernel and the second-order time-domain kernel.

[0126] Specifically, the input signal is x(n) = a1cos(2πnf1 / f s )+a2cos(2πnf2 / f s ), f s is the sampling frequency, and the output is:

[0127]

[0128] where H1(f1), H2(f2) represent the fundamental frequency signals of the linear part output; H2(f1,f1), H2(f2,f2) represent the harmonic signal components of the output of the quadratic nonlinear term; H2(f1,f2) represents the intermodulation distortion and frequency components of the output of the quadratic nonlinear term; and H2(f1,-f2) represents the intermodulation distortion difference frequency components of the output of the quadratic nonlinear term.

[0129] According to formula (9), the relationship between the frequency-domain output and the frequency-domain kernel can be represented as:

[0130]

[0131] It is worth noting that the frequency-domain coefficients of the quadratic kernel function have some symmetry relationships:

[0132]

[0133] According to formula (11), the measurement process can be simplified.

[0134] Therefore, the first-order frequency-domain kernel and the second-order frequency-domain kernel can be identified by the dual-tone method. Then, the first-order time-domain kernel and the second-order time-domain kernel can be obtained through inverse Fourier transform.

[0135] In practical applications, a harmonic signal containing two frequency components f1 and f2 is input into the parametric array system, and f1 ∈ [0.2 kHz, 0.4 kHz, …, 5.8 kHz], f2 ∈ [0.4 kHz, 0.6 kHz, …, f1 + 0.2 kHz], f1 < f2. The first-order frequency-domain kernel and the second-order frequency-domain kernel can be obtained by the dual-tone method.

[0136] S302. Use the first-order time-domain kernel and the second-order time-domain kernel to correct the third-order time-domain kernel.

[0137] In some embodiments, as Figure 5 shown, the first-order output signal y1(n) is calculated based on the first-order time-domain kernel h1(n), and the second-order output signal y2(n) is calculated based on the second-order time-domain kernel h2(n). For example, the first-order output signal y1(n) and the second-order output signal y2(n) can be calculated according to formula (6).

[0138] Then, subtract the first-order output signal y1(n) and the second-order output signal y2(n) from the desired signal E(n) to obtain the third-order desired signal d3(n). Subsequently, correct the third-order time-domain kernel h3(n) based on the third-order desired signal d3(n) until the difference e(n) between the third-order output signal y3(n) output based on the corrected third-order time-domain kernel h3(n) and the third-order desired signal d3(n) is within the target range.

[0139] Correspondingly, based on the first-order time-domain kernel, the second-order time-domain kernel, and the third-order time-domain kernel, an audible signal that is the desired signal or close to the desired signal can be output.

[0140] For example, the desired signal can be a self-demodulated audible signal collected by a data acquisition device.

[0141] As an implementation, through the NLMS algorithm, the third-order time-domain kernel is corrected based on the third-order desired signal until the difference between the third-order output signal output by the corrected third-order time-domain kernel and the desired signal is within a preset range.

[0142] Specifically, the weight coefficient of the NLMS algorithm is:

[0143]

[0144] The input vector at time n is:

[0145]

[0146] The third-order output at time n is:

[0147] y3(n) = x3(n) T h3 = h3 T x3(n) (14)

[0148] The error signal e(n) is:

[0149] e(n) = d(n) - y1(n) - y2(n) - y3(n) = d3(n) - h3 T x3(n) (15)

[0150] where d3(n) = E(n) - y1(n) - y2(n), and the mean square error is:

[0151]

[0152] The minimum mean square error criterion is to find h3 when E[e 2 (n)] is minimum. T The LMS algorithm directly uses single-sampling data e 2 (n) to replace the mean square error E[e 2 (n)], and its iterative formula is:

[0153] h3(n+1) = h3(n) + 2μe(n)x3(n) (17)

[0154] where μ is a fixed step factor used to control the speed and stability of the iterative algorithm. In order to improve the convergence speed of the algorithm, the NLMS algorithm uses a variable step factor to replace the fixed step factor, and its iterative equation is:

[0155] h3(n+1) = h3(n) + η(n)e(n)x3(n) (18)

[0156] where

[0157]

[0158] η0 is a correction step constant, and 0 < η0 < 2, which can be selected as 0.01 in practical application. Δ is a small amount introduced to prevent the stability from decreasing due to the small inner product of the input vector, which can be selected as 0.0001 in practical application. The training signal uses the input and output data obtained in the dual-tone method.

[0159] Thus, the nonlinear filter model of the identified parameter array loudspeaker is obtained.

[0160] To further verify the nonlinear filter model, a single frequency signal with a frequency component of 1 kHz and 5 kHz is used to verify the performance of the nonlinear filter model. In the verification process, the signal is modulated by the double sideband modulation method, the modulation coefficient is 0.7, and the carrier frequency is 39 kHz. The output signal is normalized based on the amplitude of the fundamental frequency component, and then the output response of the nonlinear filter is compared with the experimental results, as shown in Figure 6 To facilitate comparison, the nonlinear filter model and the Berktay model are offset in the frequency domain.

[0161] As can be seen from Figure 6 , the Berktay model can only calculate up to the second harmonic, the VF model can calculate up to the third harmonic, and the VF model is more accurate.

[0162] S303, a nonlinear filter model is constructed by using a first-order time domain kernel, a second-order time domain kernel and a modified third-order time domain kernel.

[0163] It should be noted that the first-order time domain kernel, the second-order time domain kernel and the modified third-order time domain kernel are parameters of the nonlinear filter model, so that the nonlinear filter model can simulate the audible sound signal corresponding to the input signal based on formula (6).

[0164] Specifically, the first-order output signal, the second-order output signal, and the third-order output signal whose difference with the third-order expected signal is within the target range are added to obtain and output the audible sound signal.

[0165] The signal processing method provided in the present application constructs a nonlinear filter model by using a first-order time domain kernel, a second-order time domain kernel and a third-order time domain kernel, so as to simulate the distortion of the input signal within the third order, simulate the audible sound signal corresponding to the input signal, and improve the sound quality of the parametric array loudspeaker.

[0166] Figure 7 The structure of a signal processing device provided in an embodiment of the present application is shown in the structure diagram, as Figure 7 The signal processing device 10 of the present embodiment is used to implement the operation of the signal processing device corresponding to any of the above method embodiments. The signal processing device 10 of the present embodiment comprises:

[0167] An adjusting module 11 is configured to adjust the input signal based on the audible sound signal output by the input signal until the difference between the audible sound signal output and the expected signal is within a preset range.

[0168] An output module 12 is configured to output the input signal corresponding to the audible sound signal within the preset range from the expected signal as a signal input to the parametric array loudspeaker.

[0169] The signal processing apparatus 10 provided by the embodiments of the present application can execute the method embodiments, and the specific implementation principles and technical effects can be referred to the method embodiments, which will not be repeated here.

[0170] Figure 8 A hardware structure schematic diagram of a signal processing device provided by the embodiments of the present application is shown. As shown in the figure, Figure 8 The signal processing device 20 is used to implement the operations corresponding to the signal processing device in any of the method embodiments, and the signal processing device 20 of the embodiments can include a memory 21, a processor 22 and a communication interface 23.

[0171] The memory 21 is used to store computer instructions. The memory 21 can include a random access memory (RAM), and can also include a non-volatile memory (NVM), for example, at least one disk memory, and can also be a U disk, a mobile hard disk, a read-only memory, a magnetic disk or an optical disk, etc.

[0172] The processor 22 is used to execute the computer instructions stored in the memory to implement the method in the above embodiments. For details, please refer to the related description in the foregoing method embodiments. The processor 22 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC) and the like. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the application can be directly embodied as hardware processor execution, or executed by a combination of hardware and software modules in the processor.

[0173] Optionally, the memory 21 can be independent or integrated with the processor 22.

[0174] The communication interface 23 can be connected with the processor 22. The processor 22 can control the communication interface 23 to implement the functions of receiving and sending signals.

[0175] The signal processing device provided by the embodiments of the present application can be used to execute the above method, and the implementation manner and technical effects are similar, which will not be repeated here.

[0176] The embodiment of the present application provides a signal processing system, comprising the signal processing device and the parametric array loudspeaker.

[0177] The present application also provides a computer readable storage medium, and the computer readable storage medium stores computer instructions. The computer instructions are executed by a processor to implement the method provided by the various embodiments.

[0178] The present application also provides a computer program product, which comprises computer instructions stored in a computer readable storage medium. At least one processor of a device can read the computer instructions from the computer readable storage medium, and the at least one processor executes the computer instructions to make the device implement the method provided by the various embodiments.

[0179] The embodiment of the present application also provides a chip, which comprises a memory and a processor. The memory is used to store computer instructions, and the processor is used to call and run the computer instructions from the memory, so that the device installed with the chip executes the method described in the various possible embodiments.

[0180] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit it. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features. And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A signal processing method, characterized in that, The method includes: An audible sound signal is output based on an input signal. The input signal is adjusted by a normalized minimum average adaptive filter. An audible sound signal is then output based on the adjusted input signal until the difference between the amplitude of at least a portion of the sampling points of the output audible sound signal and the amplitude of the corresponding sampling points in the desired signal is within a preset range. Both the desired signal and the input signal include multiple sampling points, and the multiple sampling points of the desired signal correspond one-to-one with the multiple sampling points of the input signal. The input signal corresponding to the audible sound signal whose difference from the desired signal is within a preset range is used as the input signal to the parametric array loudspeaker. Adjusting the input signal using a normalized least mean square adaptive filter includes: When the difference is less than the minimum value of the preset range, the weight vector coefficients of the normalized minimum mean square adaptive filter are reduced to reduce the input signal. When the difference is greater than the maximum value of the preset range, the weight vector coefficients of the normalized minimum mean square adaptive filter are increased to increase the input signal.

2. The method according to claim 1, characterized in that, Before adjusting the input signal, the method further includes: The input signal is processed based on a nonlinear filtering model to output an audible sound signal.

3. The method according to claim 1, characterized in that, Both the input signal and the desired signal include multiple sampling points, and the multiple sampling points of the input signal correspond one-to-one with the multiple sampling points of the desired signal.

4. The method according to any one of claims 1-3, characterized in that, After the difference between the output audible sound signal and the desired signal is within a preset range, the method further includes: The input signal corresponding to an audible sound signal whose difference from the desired signal is within a preset range is modulated so that the modulated signal is embedded into a high-frequency carrier wave for highly directional sound wave output.

5. The method according to claim 2, characterized in that, The process of establishing the nonlinear filtering model includes: Identify first-order and second-order time-domain kernels; The third-order time-domain kernel is modified using the first-order and second-order time-domain kernels. The nonlinear filtering model is constructed using the first-order time-domain kernel, the second-order time-domain kernel, and the modified third-order time-domain kernel.

6. The method according to claim 5, characterized in that, The identification of first-order and second-order time-domain kernels includes: The two-tone method was used to identify first-order and second-order frequency domain kernels. Based on the first-order frequency domain kernel and the second-order frequency domain kernel, the first-order time domain kernel and the second-order time domain kernel are obtained through inverse Fourier transform.

7. The method according to claim 5, characterized in that, The step of modifying the third-order time-domain kernel using the first-order and second-order time-domain kernels includes: The first-order output signal is calculated based on the first-order time-domain kernel, and the second-order output signal is calculated based on the second-order time-domain kernel; Subtract the first-order output signal and the second-order output signal from the desired signal to obtain the third-order desired signal; The third-order time-domain kernel is modified based on the third-order desired signal until the difference between the third-order output signal based on the modified third-order time-domain kernel and the third-order desired signal is within the target range.

8. The method according to claim 7, characterized in that, The third-order time-domain kernel modified based on the third-order expected signal includes: The third-order time-domain kernel is corrected based on the third-order expected signal using the normalized minimum equal division algorithm.

9. The method according to claim 7 or 8, characterized in that, The input signal is processed based on a nonlinear filtering model to output an audible sound signal, including: The first-order output signal, the second-order output signal, and the third-order output signal whose difference with the third-order desired signal is within the target range are added together to obtain and output an audible sound signal.

10. A signal processing apparatus, characterized in that, The device includes: The processing module is used to adjust the input signal based on the input signal by using a normalized minimum average adaptive filter, and output an audible signal based on the adjusted input signal until the difference between the amplitude of at least some sampling points of the output audible signal and the amplitude of the corresponding sampling points in the desired signal is within a preset range. The output module is used to take the input signal corresponding to the audible sound signal whose difference from the desired signal is within a preset range as the input signal to the parametric array loudspeaker. The processing module is specifically used to reduce the weight vector coefficients of the normalized minimum mean square adaptive filter when the difference is less than the minimum value of the preset range, so as to reduce the input signal. When the difference is greater than the maximum value of the preset range, the weight vector coefficients of the normalized minimum mean square adaptive filter are increased to increase the input signal.

11. A signal processing device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 9.

12. A signal processing system, characterized in that, Includes the signal processing device and parametric array loudspeaker as described in claim 11; The signal processing device inputs the input signal corresponding to the audible sound signal whose difference from the desired signal is within a preset range to the parametric array loudspeaker.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, are used to implement the method as described in any one of claims 1 to 9.

14. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 9.

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