Binaural cooperative active noise control method based on combined cost function

Abstract

The invention relates to the technical field of active noise control, and provides a binaural cooperative active noise control method based on a combined cost function. The objective of the invention is to solve the problems of asynchronous convergence rates of left and right ears and inconsistent steady-state errors caused by factors such as physiological structure difference of left and right ear canals, inconsistent wearing fitness of earphones and asymmetrical directivity of loudspeakers. According to the method, a combined cost function including an overall error term, an instantaneous error term and an accumulative error term is constructed, the contribution proportion of each error term is automatically adjusted through a dynamic weight adjustment mechanism, and the weight of a control filter is updated based on a gradient descent method, so that the convergence speed and the steady-state error of a left sound channel and a right sound channel tend to be consistent. The consistency and balance of the binaural active noise control performance can be improved, and the method can be suitable for application scenes such as active noise reduction earphones and intelligent cabins.

Description

A binaural cooperative active noise control method based on a combination cost function Technical Field

[0001] This invention belongs to the field of digital signal processing and noise control technology, specifically relating to a binaural cooperative active noise control method based on a combination cost function. Background Technology

[0002] Active noise control (ANC) technology cancels noise by generating an anti-noise signal with the opposite phase and equal amplitude to the original noise. It is widely used in personal audio devices such as active noise-canceling headphones and car headrest audio systems. In binaural noise cancellation scenarios, error microphones can be placed near the left and right ear canals to monitor residual noise, and corresponding secondary sound source signals can be generated using adaptive filters. Existing technologies often employ a direct extension of the Filtered-x Least Mean Square (FxLMS) algorithm, which independently constructs error minimization targets for each ear and updates the coefficients of their respective filters.

[0003] However, the aforementioned FxLMS method treats the left and right ears as independent control objects, and its cost function is usually simplified to a linear superposition of the squares of the error signals of the left and right ears, failing to fully consider the physiological requirement of the human ear as a whole auditory system for binaural sound field consistency. In practical applications, due to factors such as differences in headphone wearing posture, asymmetry in the acoustic characteristics of the left and right ear canals, and inconsistent responses of secondary speakers, the transfer functions of the secondary pathways of the left and right ears generally exhibit asymmetry. This asymmetry leads to the following: even with the same algorithm step size and other parameter configurations, the convergence speed of the left and right ear adaptive filters is still difficult to synchronize, and the steady-state residual error is also difficult to keep consistent. Specifically, during the transient convergence phase, the binaural noise suppression process is asynchronous, easily causing asymmetric sound pressure fluctuations; during the steady-state operation phase, the imbalance of residual noise energy will disrupt the spatial naturalness of the binaural sound field, reducing auditory comfort and immersion. Therefore, existing binaural active noise control methods have significant limitations in achieving collaborative noise reduction with high auditory quality. Summary of the Invention

[0004] To address the aforementioned problems, this invention proposes a binaural cooperative active noise control method based on a combined cost function. Its principle is as follows:

[0005] Error signals were collected from both the left and right ears. Based on these signals, the total error term was calculated respectively. Instantaneous error term and cumulative error term This constitutes a combined cost function. This function automatically adjusts the contribution ratio of each error component in the cost function through a dynamic weight adjustment mechanism, and updates the control filter weights based on the gradient descent method. This makes the convergence speed and steady-state error of the left and right channels more consistent.

[0006] A binaural cooperative active noise control method based on a combination cost function, the specific steps of which are as follows:

[0007] Step S1: Obtain the error signal from the left ear. Error signals at the right ear and ;

[0008] Step S2: Based on the acquired left ear error signal Error signal with right ear Calculate the total error term separately. Instantaneous error term Cumulative error term Construct a combined cost function ;

[0009] Step S3: Based on the combined cost function The filter weights are updated using gradient descent. This makes the convergence speed and steady-state error of the left and right channels more consistent.

[0010] Furthermore, the combined cost function in step S2 The expression is:

[0011]

[0012] in, The weights for the error convergence term; Weights for convergence time consistency terms; The weight of the steady-state error consistency term; This represents absolute value operations.

[0013] Furthermore, the weights , , Generated through a dynamic adjustment mechanism based on energy normalization, making This mechanism can automatically adjust the contribution ratio of the three factors: when the total error is large, Dominant for rapid noise reduction; during convergence, and The influence of this increases, thus strengthening the regulation of consistency.

[0014] Furthermore, in step S3, the present invention substitutes the gradient information of the combined cost function into the control filter weight update formula, so that the total error of synchronous optimization in each iteration is consistent with the binaural response.

[0015] The beneficial effects of the present invention are as follows: Compared with the prior art, the present invention has the following significant advantages:

[0016] Auditory coordination is significantly enhanced. This is achieved by incorporating binaural convergence synchronicity and steady-state equilibrium constraints into the cost function. It effectively overcomes the shortcomings of traditional algorithms, such as asynchronous noise reduction processes in the left and right ears and inconsistent steady-state residual noise, thereby significantly improving auditory comfort and the balance of the spatial sound field.

[0017] Parameters are automatically adjusted. This is achieved through time-varying weighting coefficients. , , With automatic adjustment, the algorithm can dynamically balance the two optimization goals of fast noise reduction and binaural coordination based on real-time binaural error signals, thereby optimizing the auditory perception experience while maintaining a good convergence speed.

[0018] It has good engineering practicality. The combined cost function of this invention consists of a total error term, an instantaneous error term, and a cumulative error term, and the calculation process only requires the addition of a limited number of weighting operations; its design can be adapted to various adaptive filtering frameworks without the need for significant adjustments to the existing architecture; the dynamic weight adjustment mechanism is based on real-time calculation of the error signal and can be implemented on general-purpose DSP or embedded hardware platforms. Attached Figure Description

[0019] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below.

[0020] Figure 1 is an overall flowchart of a binaural cooperative active noise control method based on a combined cost function according to the present invention.

[0021] Figure 2 is a schematic diagram of the structure of the binaural multi-channel active noise control system provided in an embodiment of the present invention.

[0022] Figure 3 is a schematic diagram comparing the convergence curves of the mean square error under the traditional algorithm.

[0023] Figure 4 is a schematic diagram comparing the mean square error convergence curves of the algorithm of the present invention under the combined cost function.

[0024] Figure 5 is a schematic diagram comparing the error amplitude curves under the traditional algorithm.

[0025] Figure 6 is a schematic diagram comparing the error magnitude curves of the algorithm of the present invention under the combined cost function.

[0026] Figure 7 shows the weighting coefficients of this invention. The change curve.

[0027] Figure 8 shows the weighting coefficients of this invention. The change curve.

[0028] Figure 9 shows the weighting coefficients of this invention. The change curve. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0031] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0032] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0033] As shown in Figure 2, a specific embodiment of the present invention provides a binaural cooperative active noise control method based on a combined cost function, comprising the following steps:

[0034] Step S1: The left ear error sensor acquires the error signal from the left ear. Error signals from the right ear are collected using a right ear error sensor. ;

[0035] Step S2: Based on the collected error signals, calculate the three costs used to construct the combined cost function. Evaluation item: Total error item Instantaneous error term Cumulative error term ;

[0036] Step S3: Wherein,

[0037] Step S4: To better adjust the contribution ratio of the three error components in the cost function, energy normalization is used to dynamically adjust the weighting coefficients. , , .

[0038] Step S5: First, calculate the normalized denominator. , .in, It should be a very small positive number to prevent the denominator from being zero.

[0039] Step S6: Calculate separately , , Weight:

[0040] Step S7: Construct a combined cost function using the calculated three error components and their corresponding adaptive weights: .

[0041] Step S8: The filter weights are updated using gradient descent, implementing the minimization process in step S7. First, the combined cost function is calculated. For filter weights partial derivatives .

[0042] Step S9: The formula for updating the weights of the control filter is: in, To control the update step size of the filter, the value is set to a positive value; Channel numbering for binaural noise reduction; This represents the partial derivative operator.

[0043] To verify that this invention can improve the consistency and uniformity of binaural active noise control performance, simulation experiments will be conducted. The simulation platform will be built in the MATLAB environment, with a sampling rate of... Total number of sampling points The simulation duration is 10 seconds. The primary noise is colored noise generated by filtering Gaussian white noise through a 512th-order bandpass filter. Both reference microphones receive the same noise signal.

[0044] Under the above simulation conditions, the simulation results are shown in Figures 3 to 9. Figure 3 is a curve convergence comparison diagram of the left and right ear channels when using the traditional Filtered-x Least Mean Square (FxLMS) algorithm; Figure 4 is a curve convergence comparison diagram of the corresponding binaural cooperative active noise control method based on the combined cost function of this invention. Figure 5 is a curve convergence comparison diagram of the left and right ear channels when using the traditional Filtered-x Least Mean Square (FxLMS) algorithm; Figure 6 is a curve convergence comparison diagram of the corresponding error amplitude when using the binaural cooperative active noise control method based on the combined cost function of this invention. Figures 7, 8, and 9 are the weighting coefficients, respectively. , , The curve showing the change.

[0045] The convergence performance and steady-state error of the algorithm of this invention are analyzed by referring to Figures 3 and 4. In the initial stage, the algorithm of this invention shows a significant difference in the MSE of the left and right ears. This is because the improved cost function introduces an error coupling term and an integral term, allowing the algorithm to prioritize rapid compensation for the ear with the larger error, thereby achieving rapid decay of the overall error energy. As the iteration progresses, the errors of the left and right ears gradually tend to be balanced: the difference shrinks to 1.0 × 10⁻⁶ within 1000 ms. -4 V 2 The time difference further decreased to 1.26 × 10⁻⁶ ms by 6000 ms. -5 V² indicates that the algorithm possesses good self-balancing capabilities. In contrast, while traditional algorithms have smaller mean square errors in the initial stages, their convergence speed is slower, and their final steady-state error is higher. This algorithm sacrifices initial interaural consistency for faster overall convergence speed and achieves superior binaural equalization accuracy compared to traditional algorithms in the steady-state phase, providing a better choice for applications requiring fast response and high steady-state performance.

[0046] Furthermore, the time-domain evolution of the error amplitude is analyzed. Figure 5 shows the left ear error amplitude during the convergence process, from 2.5 × 10⁻⁶. -3 It gradually decreases to approximately -0.5 × 10⁻⁶. -3However, the error amplitude of the right ear remained at a high level and converged slowly, with significant fluctuations even up to 9500 ms. The significant difference in error amplitude between the two ears indicates that the traditional algorithm struggles to effectively balance the binaural channels, resulting in a large residual noise in the right ear and limiting the overall noise reduction performance of the system. Figure 6 shows that after adopting the improved combined cost function, the error amplitudes of the left and right ears converged synchronously to near zero within approximately 4000 ms, and the error amplitude curves of the two ears highly overlapped with minimal fluctuations. This demonstrates that the algorithm, by dynamically adjusting the weighting factors, rapidly reduces the error amplitude of both ears, shortens the convergence time by approximately 50% compared to the traditional algorithm, approaches zero error amplitude in the steady-state stage, significantly improves the noise reduction depth, and maintains consistent error amplitudes between the left and right ears, avoiding the channel imbalance problem in the traditional algorithm and preserving complete spatial sound field information for binaural hearing applications.

[0047] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0048] In the embodiments provided in this application, it should be understood that the division of units is only a logical functional division. In actual implementation, there may be other division methods, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored.

[0049] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A binaural cooperative active noise control method based on a combined cost function, the method comprising the following steps: Step S1: Obtain the error signal from the left ear. Error signal at the right ear ,in For discrete-time indexing; Step S2: Based on the acquired left ear error signal Error signal with right ear Calculate the total error term separately. Instantaneous error term Cumulative error term Construct a combined cost function Step S3: Based on the combined cost function The gradient descent method is used to update the control filter weights. This makes the convergence speed and steady-state error of the left and right channels more consistent.

2. The method according to claim 1, characterized in that, The combined cost function in step S2 Including total error term Instantaneous error term Cumulative error term ,in: The weights for the error convergence term; Weights for convergence time consistency terms; The weight of the steady-state error consistency term; This represents absolute value operations.

3. The formula according to claim 2, characterized in that, Weighting coefficient 、 、 It can be generated based on a dynamic adjustment mechanism of energy normalization, and is defined as follows: To normalize the denominator, the definition is: in, It is a very small positive number.

4. The method according to claim 1, characterized in that, Step S3 involves minimizing the cost function. To adjust the weights of the control filter Filter weight update formula: in, To control the update step size of the filter, the value is set to a positive value; Channel numbering for binaural noise reduction; This represents the partial derivative operator.

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