Real-time adaptive active noise reduction method and adaptive active noise reduction earphone

Abstract

The invention provides a real-time adaptive active noise reduction method and an adaptive active noise reduction earphone. The method comprises the following steps: obtaining a first transfer function from a feed-forward channel to a feedback channel in a state of closing active noise reduction; obtaining a second transfer function from the loudspeaker to the feedback channel in a state of starting active noise reduction; obtaining a characteristic value based on the second transfer function so as to determine the wearing state of the current earphone, and updating the parameters of the IIR filter according to the wearing state; and acquiring an updated first parameter of the IIR filter on the feed-forward channel and an updated second parameter of the IIR filter on the feedback channel, and updating the parameters of the FIR filter based on the signal acquired by the feed-forward microphone, the first transfer function, the first parameter, the second parameter and the second transfer function. In this way, the active noise reduction state can be adjusted in real time, and a good active noise reduction effect is achieved for different wearing states of the earphone.

Description

Technical Field

[0001] This application relates to the technical field of earphones, and particularly to a method for real-time adaptive active noise cancellation and an adaptive active noise cancellation earphone. Background Art

[0002] ANC noise cancellation (Active Noise Control) is a technology for suppressing environmental noise. Generally, the parameters of an adaptive IIR filter are selected through a prompt tone. The noise signal and the prompt tone signal are collected by a sensor (usually a microphone). The control algorithm generates a reverse sound wave with a phase opposite to that of the noise, and superimposes it on the audio signal to cancel the environmental noise, thereby achieving the suppression or elimination of external noise.

[0003] However, since the parameters of the adaptive IIR filter are several fixed groups, the best noise cancellation effect cannot be achieved for different wearing positions. Moreover, the existing method only runs once when the earphone is worn for the first time and active noise cancellation is turned on to determine the current wearing state, and selects the corresponding parameters of the adaptive filter based on this wearing state. But if the wearing position of the earphone is adjusted later, the parameters of the adaptive filter cannot be updated in real time, resulting in a deterioration of the noise cancellation effect. Summary of the Invention

[0004] In view of the above technical problems existing in the prior art, this application is proposed. This application aims to provide a method for real-time adaptive active noise cancellation and an adaptive active noise cancellation earphone.

[0005] According to the first aspect of this application, a method for real-time adaptive active noise cancellation for an active noise cancellation earphone is provided. The active noise cancellation earphone includes a feedforward microphone, a feedback microphone, a speaker, and a filter. The filter includes an IIR filter and an FIR filter. The method includes: in the state of turning off active noise cancellation, obtaining a first transfer function from the feedforward channel to the feedback channel based on the signals collected by the feedforward microphone and the feedback microphone; in the state of starting active noise cancellation, obtaining a second transfer function from the speaker to the feedback channel based on the first transfer function and the signal collected by the speaker; obtaining an eigenvalue based on the second transfer function to determine the current wearing state of the earphone based on the eigenvalue, and updating the parameters of the IIR filter according to the wearing state; obtaining an updated first parameter of the IIR filter on the feedforward channel and an updated second parameter of the IIR filter on the feedback channel, and updating the parameters of the FIR filter based on the signal collected by the feedforward microphone, the first transfer function, the first parameter, the second parameter, and the second transfer function.

[0006] According to the second solution of the present application, an adaptive active noise cancelling headphone is provided, which includes a processor, a feedforward microphone, a feedback microphone, a speaker and a filter. Among them, the filter includes an IIR filter and an FIR filter. The processor is configured to: in the state of turning off active noise cancellation, obtain a first transfer function from the feedforward channel to the feedback channel based on the signals collected by the feedforward microphone and the feedback microphone; in the state of starting active noise cancellation, obtain a second transfer function from the speaker to the feedback channel based on the first transfer function and the signal collected by the speaker; obtain eigenvalues based on the second transfer function to determine the wearing state of the current headphone based on the eigenvalues, and update the parameters of the IIR filter according to the wearing state; obtain the updated first parameter of the IIR filter on the feedforward channel and the updated second parameter of the IIR filter on the feedback channel, and update the parameters of the FIR filter based on the signal collected by the feedforward microphone, the first transfer function, the first parameter, the second parameter, and the second transfer function.

[0007] According to the third solution of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores computer instructions for causing the computer to execute the method of real-time adaptive active noise cancellation as described in various embodiments of the present application.

[0008] According to the fourth solution of the present application, a computer program product is provided, including computer instructions for causing a computer to execute the method of real-time adaptive active noise cancellation as described in various embodiments of the present application.

[0009] Compared with the prior art, the beneficial effects of the embodiments of the present application are as follows:

[0010] The method provided by the embodiment of the present application, in the state of turning off active noise cancellation, based on the signals collected by the feedforward microphone and the feedback microphone, obtains the first transfer function from the feedforward channel to the feedback channel. In the state of starting active noise cancellation, based on the first transfer function and the signal collected by the speaker, obtains the second transfer function from the speaker to the feedback channel, and obtains the eigenvalue based on the second transfer function, so as to determine the wearing state of the current earphone based on the eigenvalue, and update the parameters of the IIR filter according to the wearing state. On the basis of updating the parameters of the IIR filter, the parameters of the FIR filter are optimized, and the parameters of the FIR filter are updated based on the signals collected by the feedforward microphone, the first transfer function, the first parameter, the second parameter, and the second transfer function. In this way, by continuously monitoring the data changes of the feedforward microphone, the feedback microphone, and the speaker, the parameters of the IIR filter and the FIR filter can be updated in real time. When the user makes any position change during the process of wearing the earphone, the ANC state can be adjusted in real time, and a good noise cancellation effect can be always guaranteed during the wearing process.

[0011] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above description and other purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application are specifically exemplified below. Brief Description of the Drawings

[0012] In the drawings which are not necessarily drawn to scale, the same reference numerals may describe similar components in different views. Similar reference numerals with alphabetic suffixes or different alphabetic suffixes may represent different examples of similar components. The drawings generally illustrate various embodiments by way of example and not by way of limitation, and are used together with the description and the claims to illustrate the disclosed embodiments. Such embodiments are illustrative and exemplary and are not intended to be exhaustive or exclusive embodiments of the method, apparatus, system, or non-transitory computer-readable medium having instructions for implementing the method.

[0013] Figure 1 Shows a schematic diagram of the active noise cancellation structure of an active noise cancellation earphone according to an embodiment of the present application.

[0014] Figure 2 Shows a schematic flow chart of a method for real-time adaptive active noise cancellation according to an embodiment of the present application.

[0015] Figure 3 Shows a schematic flow chart of real-time updating of the ANC state according to an embodiment of the present application. Detailed Description of the Embodiments

[0016] To enable those skilled in the art to better understand the technical solution of the present application, the present application will be described in detail below with reference to the accompanying drawings and specific embodiments. The embodiments of the present application will be further described in detail below with reference to the accompanying drawings and specific examples, but it is not a limitation to the present application.

[0017] The "first", "second" and similar terms used in the present application do not indicate any order, quantity or importance, but are only used for distinction. The terms such as "including" or "comprising" used in the present application mean that the elements before the term cover the elements listed after the term, and do not exclude the possibility of also covering other elements. In the present application, the arrows shown in the figures for each step are only examples of the execution order and not a limitation. The technical solution of the present application is not limited to the execution order described in the embodiments. The steps in the execution order can be combined, decomposed, and the order can be swapped as long as the logical relationship of the execution content is not affected.

[0018] All terms used in the present application (including technical terms or scientific terms) have the same meaning as understood by those of ordinary skill in the art to which the present application belongs, unless otherwise specifically defined. It should also be understood that terms defined in a general dictionary, such as those, should be interpreted as having a meaning consistent with their meaning in the context of the relevant art, and should not be interpreted in an idealized or overly formal sense, unless specifically defined as such here. Technologies and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, such technologies and devices should be regarded as part of the specification.

[0019] In some embodiments of the present application, a method for real-time adaptive active noise cancellation is provided, and this method is used for active noise-canceling headphones. As Figure 1 shown, the active noise-canceling headphones include a feedforward microphone 101, a feedback microphone 102, a speaker 105 and a filter. Among them, the filter includes an IIR filter 103 and an FIR filter 104.

[0020] In Figure 1 the shown audio processing path, the feedforward microphone 101 and the feedback microphone 102 are used to collect external audio signals. The feedforward microphone 101 is usually used to collect ambient noise, and the feedback microphone 102 focuses on collecting the audio leaked from the speaker 105. The audio signals collected by the feedforward microphone 101 and the feedback microphone 102 respectively enter the IIR filter 103 and the FIR filter 104 for filtering processing, and the signals after filtering processing are respectively combined. The combined signals from the feedforward path and the feedback path are further combined, and are mixed with the audio signal, and are played through the speaker 105.

[0021] This is only an exemplary illustration and does not constitute a limitation on the specific solution.

[0022] In this embodiment, the method of real-time adaptive active noise reduction is as Figure 2 shown in steps S201 - S204. The arrows shown in the figure for each step are only examples of the execution order and not limitations. The technical solution of this application is not limited to the execution order described in the embodiment. The steps in the execution order can be combined, decomposed, or reordered as long as the logical relationship of the execution content is not affected.

[0023] Among them, an earphone with active noise reduction function may have several different noise reduction modes, such as low-level noise reduction mode, medium-level noise reduction mode, deep-level noise reduction mode, or other noise reduction modes, which are not limited herein.

[0024] Specifically, several sets of fixed parameters can be set for the IIR filter of the earphone, and each set of parameters corresponds to a different noise reduction mode. For example, the parameters of the adaptive IIR filter with good noise reduction effect when the earphone is in different wearing states can be collected by using the feedforward microphone, feedback microphone, and speaker, and the parameters can be written into the earphone Flash memory to pre-configure a preset group of parameters for the IIR filter. Each preset group of parameters is used to perform noise reduction processing on the earphone in different wearing states.

[0025] Among them, the parameters can be frequency response parameters. During the process of configuring the preset group of parameters, they can be set according to different wearing states of the earphone. For example, when the earphone is in a loose wearing state, a state of wearing tightly against the ear, or other wearing states, there are corresponding different parameter groups. Each preset group of parameters is also used to perform different levels of noise reduction processing on the earphone in different wearing states.

[0026] In this embodiment, each preset group of parameters can also be used to illustrate the noise reduction mode of the IIR filter when the earphone is in different wearing states.

[0027] In this embodiment, when the earphone is powered on and the active noise reduction is turned off, the signals of the feedforward microphone, feedback microphone, and speaker are collected simultaneously to detect the wearing state of the earphone. That is, when the earphone is powered on, the algorithm is initialized, and at the same time, the voice signal of the feedforward microphone, the voice signal of the feedback microphone, and the speaker playback signal of the earphone are collected in real time. These three signals are all converted to the frequency domain through fast Fourier transform operations.

[0028] Detect the tightness of the headphone wearing based on the collected signals, and according to the detected wearing state of the headphones, set the parameters of the IIR filter to the parameters of a preset group that matches the current headphone wearing state (as the initial parameters), that is, the noise reduction mode that matches the current headphone wearing state is the initial noise reduction mode.

[0029] That is to say, in the state of activating active noise reduction, set the parameters of the IIR filter to a set of parameters in the preset group as the initial parameters based on the wearing state of the headphones, so as to determine the wearing state of the current headphones based on the eigenvalue, and update the initial parameters of the IIR filter according to the wearing state.

[0030] In this embodiment, in step S201, in the state of turning off active noise reduction, based on the signals collected by the feedforward microphone and the feedback microphone, obtain the first transfer function from the feedforward channel to the feedback channel.

[0031] When the active noise reduction function of the headphones is not turned on, it indicates that the active noise reduction function of the headphones does not play a role at this time, and the additional signal processing interference introduced by the active noise reduction algorithm can be avoided. Here, the feedforward channel can be understood as the transmission path of the signals collected by the feedforward microphone, and the feedback channel is the transmission path of the signals collected by the feedback microphone. By analyzing the signals collected by the feedforward microphone (input) and the feedback microphone (output) and using signal processing methods such as Fourier transform, the transfer function from the feedforward channel to the feedback channel can be obtained, which reflects how the signals at the feedforward microphone end affect the signals at the feedback microphone end.

[0032] Among them, the first transfer function P(z) from the feedforward channel to the feedback channel = FB(z) / FF(z), where FB(z) represents the frequency-domain signal of the feedback microphone, and FF(z) represents the frequency-domain signal of the feedforward microphone.

[0033] In step S201, in the state of activating active noise reduction, based on the first transfer function and the signals collected by the speaker, obtain the second transfer function from the speaker to the feedback channel. Specifically, when activating active noise reduction, initially judge the wearing state of the headphones based on the sound signals collected by the feedforward microphone, the feedback microphone and the speaker, and set the parameters of the IIR filter to the initial parameters according to the current wearing state of the headphones. At this time, the noise reduction mode of the current headphones is the initial noise reduction mode.

[0034] Among them, the current wearing state of the headphones can be initially judged according to the leakage amount of the noise. For example, when the leakage amount of the noise is within the first threshold range, the wearing state of the headphones is the first state; when the leakage amount is within the second threshold range, the wearing state of the headphones is the second state; when the leakage amount is within the third threshold range, the wearing state of the headphones is the third state.

[0035] This is only for illustrative purposes and does not constitute a limitation on specific solutions.

[0036] After the active noise cancellation function is activated, the system will generate an inverted compensation signal through an algorithm based on the signals collected by the feedforward microphone and the feedback microphone, and play it out through the speaker to cancel the ambient noise. At this time, the signal collected by the feedback microphone not only contains the signal originally transmitted through the feedforward channel, but also contains the compensation signal played by the speaker and the result of their interaction.

[0037] In the active noise cancellation state, the signal emitted by the speaker is a compensation signal generated to achieve noise cancellation. Collecting the signal of the speaker can understand the output generated by the system to cancel the noise. By analyzing this signal and its relationship with the signal of the feedback microphone, it can help analyze the influence of the speaker signal on the feedback channel.

[0038] Specifically, the second transfer function S(z) = [FB(z) - FF(z) × P(z)] / SPK(z), where SPK(z) represents the frequency-domain signal of the speaker. Thus, this second transfer function describes how the signal emitted by the speaker affects the signal collected by the feedback channel in the active noise cancellation state.

[0039] In step S203, eigenvalues are obtained based on the second transfer function to determine the wearing state of the current earphone based on the eigenvalues, and the parameters of the IIR filter are updated according to the wearing state.

[0040] Specifically, some statistical features of the frequency response can be calculated based on the second transfer function, such as the mean, variance, standard deviation, etc. For example, by calculating the average value of the frequency response amplitude within a certain frequency range, the average gain or attenuation degree of the system for the signals in this frequency band can be obtained, and this average value can be used as the eigenvalue.

[0041] Alternatively, frequency response analysis can also be performed on the second transfer function to determine the peak value as the eigenvalue. This is only for illustrative purposes and does not constitute a specific limitation on the eigenvalue.

[0042] In this embodiment, determining the wearing state of the current earphone based on the eigenvalues specifically includes judging the wearing state of the current earphone according to the result of comparing the eigenvalues with a threshold.

[0043] For example, it is set that when the difference between the eigenvalue and the threshold is within the first preset value range, the wearing state of the earphone is the first state; when the difference is within the second preset value range, the wearing state of the earphone is the second state; when the difference is within the third preset value range, the wearing state of the earphone is the third state. This is only for illustrative purposes and does not constitute a limitation on specific solutions.

[0044] That is, the wearing state of the current earphone can be determined based on the eigenvalue, and the parameters of the IIR filter can be updated according to the updated wearing state.

[0045] For example, after active noise cancellation is started, the parameters of the IIR filter are initial parameters (assuming that the initial parameters correspond to the first state of the wearing state of the earphone). However, if the wearing state of the earphone determined based on the eigenvalue is the second state, then the parameters of the IIR filter are updated according to the updated wearing state of the earphone at this time.

[0046] In step S204, the updated first parameter of the IIR filter on the feedforward channel and the updated second parameter of the IIR filter on the feedback channel are obtained, and the parameters of the FIR filter are updated based on the signal collected by the feedforward microphone, the first transfer function, the first parameter, the second parameter, and the second transfer function.

[0047] For example, the signal collected by the feedforward microphone can be processed by the feedforward channel IIR filter (whose filtering characteristics are determined by the first parameter), and then combined with the signal relationship between channels described by the first transfer function and the second transfer function, as well as the effect of the feedback channel IIR filter (whose filtering characteristics are determined by the second parameter), to determine how to update the parameters of the FIR filter so that the system can achieve better noise cancellation effect or more accurate audio signal restoration. This process can be an iterative process. As new input signals keep coming, the parameters of the IIR filter and the FIR filter will be continuously updated and optimized to adapt to the changing signal environment, so that the earphone can have a good noise cancellation effect when in different positions.

[0048] The method provided by the embodiments of the present application updates the parameters of the IIR filter in real time and updates the parameters of the FIR filter at the same time, thereby adjusting the ANC state in real time and achieving a good noise cancellation effect for different wearing positions. This means that when any changes occur during the user's wearing of the earphone (such as head movement, earphone loosening, etc.), the parameters of the IIR filter and the FIR filter can be adjusted in real time to ensure the best noise cancellation effect.

[0049] Specifically, the parameters of the FIR filter can be updated according to formula (1):

[0050]

[0051] In formula (1), FB(z) represents the frequency-domain signal of the feedback microphone, FF(z) represents the frequency-domain signal of the feedforward microphone, P(z) represents the first transfer function, IIRFF represents the updated first parameter of the IIR filter on the feedforward path, IIR FB represents the updated second parameter of the IIR filter on the feedback path, S(z) represents the second transfer function, W represents the parameter of the FIR filter, where the error is related to FB(z).

[0052] That is, using the frequency signal FB(z) of the feedback microphone as the error for gradient descent update, adjusting the parameter of the FIR filter, the parameter of the FIR filter corresponding to when the error is less than the threshold is the updated parameter of the FIR filter, that is, when the error is less than the threshold, the update stops, and at this time the earphone is in a stable state, and it can be considered that the update of the FIR filter parameters is completed.

[0053] In some other embodiments, the parameters of the FIR filter can be optimized by the LMS (Least Mean Square) algorithm. The goal of the LMS algorithm is to minimize the expected value of the square of the error, which is achieved by the gradient descent method, that is, updating along the negative gradient direction of the square of the error with respect to the parameters W of the FIR filter, gradually approaching the minimum value of the square of the error, as shown in formula (2):

[0054]

[0055] In formula (2),

[0056] and formula (1) is updated according to formula (3):

[0057] W(n + 1) = W(n) + μ * FB(z) * FF′ formula (3);

[0058] In formula (3), n represents the current time, n + 1 represents the next time of the current time, μ represents the update step size, which controls the amplitude of each update. If the value of μ is too large, the parameter update may skip the optimal value, resulting in the algorithm not converging; if the value of μ is too small, the speed of parameter update will be very slow, and the time for the algorithm to converge to the optimal value will be very long. W(n) is the parameter of the FIR filter at the current time, and W(n + 1) is the parameter of the updated FIR filter at the next time.

[0059] By the LMS algorithm, the parameters W of the FIR filter are continuously adjusted using the gradient descent method to minimize the expected value of the square of the error. Specifically, the partial derivative of the square of the error with respect to the coefficient is calculated according to formula (2) to determine the update direction, and then according to formula (3), combined with the update step size μ, the parameters of the FIR filter are updated. As time goes by, the parameters of the FIR filter will gradually converge to the optimal value, thereby realizing the optimization of the performance of the FIR filter.

[0060] Based on the audio signals collected by the feedforward microphone, feedback microphone, and speaker, calculate S(z) in real time and update the noise reduction mode of the IIR filter and the state of the FIR filter.

[0061] Exemplarily, as Figure 3 , in step S301, the feedforward microphone (FF-MIC) and feedback microphone (FB-MIC) collect sound. In step S302, active noise reduction is turned off, and then step S303 is executed to calculate the first transfer function. At the same time, step S304 is executed to determine whether active noise reduction is enabled. If the determination result is no, then step S305 is executed to maintain the existing state, that is, the filter parameters are not updated. If the determination result is yes, then step S306 is continued. Detect the current wearing state of the headset based on the sound collected by the FF-MIC and FB-MIC, and determine the current wearing state of the headset according to the detection result. Based on the latest determined wearing state of the headset, determine that the noise reduction mode of the IIR filter is the first mode. Then continue to execute step S307 to calculate the second transfer function, and determine the eigenvalue based on the second transfer function, and then continue to determine whether the eigenvalue is greater than the threshold (such as step S308).

[0062] If the determination result of step S308 is no, it means that the current wearing state of the headset is close to the wearing state detected when the headset is started, and there is no obvious position change. At this time, there is no need to update the IIR filter parameters, and step S309 is continued to calculate the parameters of the FIR filter to update the parameters of the FIR filter, thereby updating the ANC state (step S311).

[0063] Of course, if the determination result of step S308 is yes, it means that the position of the current wearing state of the headset has changed compared to the initial state, then step S310 is continued. Switch the noise reduction mode of the IIR filter to the second mode according to the latest determined wearing state of the headset, and then continue to execute step S309.

[0064] That is to say, continuously collect the signals of the feedforward microphone, feedback microphone, and speaker to continuously monitor the changes in the frequency domain signals of the feedforward microphone, feedback microphone, and speaker, and update the parameters of the IIR filter and FIR filter based on the changes in the frequency domain signals. This real-time adaptive adjustment ensures that the user can always enjoy good noise reduction effects regardless of any changes in the position of the headset during the wearing process.

[0065] In some other embodiments of the present application, an adaptive active noise-canceling headphone is provided, which includes a processor, a feedforward microphone, a feedback microphone, a speaker, and a filter. Among them, the filter includes an IIR filter and an FIR filter. The processor is configured to: in the state where active noise cancellation is turned off, obtain a first transfer function from the feedforward channel to the feedback channel based on the signals collected by the feedforward microphone and the feedback microphone; in the state where active noise cancellation is started, obtain a second transfer function from the speaker to the feedback channel based on the first transfer function and the signal collected by the speaker; obtain eigenvalues based on the second transfer function to determine the wearing state of the current headphone based on the eigenvalues, and update the parameters of the IIR filter according to the wearing state; obtain an updated first parameter of the IIR filter on the feedforward channel and an updated second parameter of the IIR filter on the feedback channel, and update the parameters of the FIR filter based on the signal collected by the feedforward microphone, the first transfer function, the first parameter, the second parameter, and the second transfer function.

[0066] In this way, by continuously monitoring the data changes of the feedforward microphone, the feedback microphone, and the speaker, the parameters of the IIR filter and the FIR filter can be updated in real time. When the user makes any position change during the process of wearing the headphone, the ANC state can be adjusted in real time, and a good noise-canceling effect can be always ensured during the wearing process.

[0067] In some other embodiments of the present application, the processor is further configured to: continuously collect the signals of the feedforward microphone, the feedback microphone, and the speaker to continuously monitor the change conditions of the frequency-domain signals of the feedforward microphone, the feedback microphone, and the speaker, and update the parameters of the IIR filter and the FIR filter based on the change conditions of the frequency-domain signals.

[0068] The adaptive active noise-canceling headphone provided by this embodiment enhances the noise-canceling effect by updating the parameters of the parallel FIR filter based on the parameter update of the IIR filter. The IIR filter is responsible for providing the basic noise-canceling frequency response curve, while the update of the FIR filter parameters is used to further optimize the noise-canceling performance to ensure that the headphone can maintain an excellent noise-canceling effect in various wearing states.

[0069] The processor may be a processing device including more than one general-purpose processing device, such as a microprocessor, a central processing unit (CPU), a graphics processing unit (GPU), etc. More specifically, the processor may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor running other instruction sets, or a processor running a combination of instruction sets. The processor may also be more than one dedicated processing device, such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), a system on a chip (SoC), etc.

[0070] According to an embodiment of the present application, there is also provided a computer-readable storage medium storing computer instructions for causing a computer to execute the steps of the real-time adaptive active noise reduction method as described in various embodiments of the present application.

[0071] The above computer-readable storage medium may be, for example, a read-only memory (ROM), a random access memory (RAM), a phase change random access memory (PRAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), an electrically erasable programmable read-only memory (EEPROM), other types of random access memories (RAM), a flash drive or other forms of flash memory, a cache, a register, a static memory, a compact disc read-only memory (CD-ROM), a digital versatile disc (DVD) or other optical memories, a cassette tape or other magnetic storage devices, or any other possible non-transitory medium used to store information or instructions accessible by a computer device.

[0072] According to an embodiment of the present application, there is also provided a computer program product including computer instructions for causing a computer to execute the steps in the real-time adaptive active noise reduction method as described in various embodiments of the present application.

[0073] The present application describes various operations or functions that may be implemented as software code or instructions or defined as software code or instructions. Such content may be source code that can be directly executed or differential code (“incremental” or “patch” code) (“object” or “executable” form). The software code or instructions may be stored in a computer-readable storage medium and, when executed, may cause a machine to execute the described functions or operations, and include any mechanism for storing information in a form accessible by a machine (e.g., a computing device, an electronic system, etc.), such as a recordable or non-recordable medium (e.g., read-only memory (ROM), random access memory (RAM), magnetic disk storage media, optical storage media, flash devices, etc.).

[0074] Moreover, although exemplary embodiments have been described herein, the scope includes any and all embodiments based on the present application that have equivalent elements, modifications, omissions, combinations (e.g., schemes that cross various embodiments), adaptations, or alterations. The elements in the claims will be broadly interpreted based on the language employed in the claims and are not limited to the examples described in this specification or during the implementation of the present application, and the examples will be construed as non-exclusive. Thus, the specification and examples are intended to be considered only as examples, and the true scope and spirit are indicated by the following claims and the full scope of their equivalents.

[0075] The above description is intended to be illustrative rather than restrictive. For example, the above examples (or one or more of their schemes) can be used in combination with each other. For example, those of ordinary skill in the art can use other embodiments when reading the above description. Additionally, in the above detailed description, various features can be grouped together to simplify the present application. This should not be construed as an intention that a disclosed feature that is not claimed is necessary for any claim. On the contrary, the subject matter of the present application can be less than all the features of a particular disclosed embodiment. Thus, the claims are incorporated herein as examples or embodiments into the detailed description, where each claim independently serves as a separate embodiment, and it is contemplated that these embodiments can be combined with each other in various combinations or permutations. The scope of the present application should be determined with reference to the appended claims and the full scope of the equivalent forms empowered by these claims.

[0076] The above embodiments are only exemplary embodiments of the present application and are not used to limit the present application. The protection scope of the present application is defined by the claims. Those skilled in the art can make various modifications or equivalent replacements within the essence and protection scope of the present application, and such modifications or equivalent replacements should also be regarded as falling within the protection scope of the present application.

Claims

1. A real-time adaptive active noise reduction method for active noise reduction headphones, characterized in that: The active noise reduction headset includes a feedforward microphone, a feedback microphone, a speaker and a filter, wherein the filter includes an IIR filter and a FIR filter, and the method includes: In a state where active noise reduction is turned off, a first transfer function from the feedforward channel to the feedback channel is obtained based on a signal collected by a feedforward microphone and a signal collected by a feedback microphone; In a state where active noise reduction is started, obtaining a second transfer function from the speaker to the feedback channel based on the first transfer function and a signal collected by the speaker; Obtaining a characteristic value based on the second transfer function, determining a current wearing state of the headset based on the characteristic value, and updating parameters of the IIR filter according to the wearing state; Acquire the updated first parameters of the IIR filter on the feedforward channel and the updated second parameters of the IIR filter on the feedback channel, and update the parameters of the FIR filter based on the signal collected by the feedforward microphone, the first transfer function, the first parameter, the second parameter, and the second transfer function.

2. The method according to claim 1, characterized in that Updating the parameters of the FIR filter specifically includes: The frequency signal of the feedback microphone is used as an error to perform gradient descent update, and the parameters of the FIR filter are adjusted. When the error is less than a threshold, the corresponding parameters of the FIR filter are the update parameters of the FIR filter.

3. The method according to claim 1 or 2, characterized in that: The method further comprises: The signals of the feedforward microphone, the feedback microphone and the speaker are continuously collected to continuously monitor the changes of the frequency domain signals of the feedforward microphone, the feedback microphone and the speaker, and the parameters of the IIR filter and the FIR filter are updated based on the changes of the frequency domain signals.

4. The method according to claim 1, characterized in that The method further comprises: Pre-configuring parameters of preset groups for the IIR filter, the parameters of each preset group being used to perform noise reduction processing on headphones in different wearing states; When the headset is turned on and active noise reduction is turned off, the signals of the feedforward microphone, feedback microphone and speaker are collected simultaneously to detect the wearing state of the headset; When active noise reduction is started, the parameters of the IIR filter are set to a set of parameters in the preset group as initial parameters based on the wearing state of the earphone, so that the wearing state of the current earphone is determined based on the characteristic value, and the initial parameters of the IIR filter are updated according to the wearing state.

5. The method according to claim 1, characterized in that According to formula (1), the parameters of the FIR filter are updated: In formula (1), FB(z) represents the frequency domain signal of the feedback microphone, FF(z) represents the frequency domain signal of the feedforward microphone, P(z) represents the first transfer function, IIR FF represents the updated first parameter of the IIR filter in the feedforward path, IIR FB denotes the updated second parameters of the IIR filter in the feedback path, S(z) denotes the second transfer function, and W denotes the parameters of the FIR filter.

6. The method according to claim 5, characterized in that The parameters of the FIR filter are optimized according to formula (2): In formula (2), And update formula (1) according to formula (3): W(n+1)=W(n)+μ*FB(z)*FF′ Formula (3); In formula (3), n represents the current time, n+1 represents the next time after the current time, and μ represents the update step size.

7. An adaptive active noise reduction headset, characterized in that: The invention comprises a processor, a feedforward microphone, a feedback microphone, a speaker and a filter, wherein the filter comprises an IIR filter and a FIR filter, and the processor is configured as follows: In a state where active noise reduction is turned off, a first transfer function from the feedforward channel to the feedback channel is obtained based on a signal collected by a feedforward microphone and a signal collected by a feedback microphone; In a state where active noise reduction is started, obtaining a second transfer function from the speaker to the feedback channel based on the first transfer function and a signal collected by the speaker; Obtaining a characteristic value based on the second transfer function, determining a current wearing state of the headset based on the characteristic value, and updating parameters of the IIR filter according to the wearing state; Acquire the updated first parameters of the IIR filter on the feedforward channel and the updated second parameters of the IIR filter on the feedback channel, and update the parameters of the FIR filter based on the signal collected by the feedforward microphone, the first transfer function, the first parameter, the second parameter, and the second transfer function.

8. The adaptive active noise reduction headset according to claim 7, characterized in that: The processor is further configured to: continuously collect signals from the feedforward microphone, feedback microphone and speaker to continuously monitor changes in the frequency domain signals of the feedforward microphone, feedback microphone and speaker, and update the parameters of the IIR filter and the FIR filter based on changes in the frequency domain signals.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the real-time adaptive active noise reduction method according to any one of claims 1 to 8.

10. A computer program product, characterized in that The method comprises computer instructions, wherein the computer instructions are used to enable a computer to execute the real-time adaptive active noise reduction method according to any one of claims 1 to 8.

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