CN114391166A

CN114391166A - Active noise reduction method, active noise reduction device and active noise reduction system

Info

Publication number: CN114391166A
Application number: CN202080005894.7A
Authority: CN
Inventors: 张立斌
Original assignee: Huawei Technologies Co Ltd
Current assignee: Huawei Technologies Co Ltd
Priority date: 2020-08-04
Filing date: 2020-08-04
Publication date: 2022-04-22
Also published as: WO2022027208A1

Abstract

An active noise reduction method, an active noise reduction device and an active noise reduction system, an active noise reduction earphone applying the active noise reduction method, a vehicle-mounted headrest device and an automobile comprising the active noise reduction device. The active noise reduction method comprises the following steps: acquiring a target audio signal, an environment audio signal and an initial reference signal of a user, wherein the target audio signal is used for representing audio information concerned by the user, the environment audio signal is used for representing audio information of an environment where the user is located, and the initial reference signal is used for carrying out active noise reduction processing on the environment audio signal (S210); obtaining a target reference signal according to the target audio signal and the initial reference signal, wherein the target reference signal does not include the target audio signal (S220); a noise reduction signal is obtained according to the target reference signal and the ambient audio signal, and the noise reduction signal is used for offsetting the target reference signal (S230). The method can perform selective noise reduction processing according to the requirements of the user, so that the active noise reduction method meets the personalized requirements of the user.

Description

Active noise reduction method, active noise reduction device and active noise reduction system

Technical Field

The present application relates to the field of active noise reduction, and in particular, to an active noise reduction method, an active noise reduction apparatus, and an active noise reduction system.

Background

Active Noise Cancellation (ANC) is based on the principle of sound wave superposition, and noise removal is realized by mutual cancellation of sound waves.

Currently, any external audio signal is suppressed in active noise reduction systems; however, in general, users still want to effectively perceive audio information of interest to themselves; such as audio signals of interest to the user or real-time audio signals associated with the demand.

Therefore, how to avoid reducing noise of any external sound signal during active noise reduction becomes a problem to be solved urgently, so that the active noise reduction method meets the personalized requirements of users.

Disclosure of Invention

The application provides an active noise reduction method, an active noise reduction device and an active noise reduction system, so that a noise reduction signal after active noise reduction processing still comprises a target audio signal concerned by a user, and personalized requirements of the user can be met.

In a first aspect, an active noise reduction method is provided, including:

acquiring a target audio signal, an environment audio signal and an initial reference signal of a user, wherein the target audio signal is used for representing audio information concerned by the user, the environment audio signal is used for representing audio information of an environment where the user is located, and the initial reference signal is used for representing active noise reduction processing on the environment audio signal; obtaining a target reference signal according to the target audio signal and the initial reference signal, wherein the target reference signal does not include the target audio signal; and obtaining a noise reduction signal according to the target reference signal and the environment audio signal, wherein the noise reduction signal is used for offsetting the target reference signal.

It should be understood that the target audio signal may be different for different users. The target audio signal may refer to an audio signal of interest to the user; for example, the target audio signal may refer to an audio signal of interest to a user in the ambient audio signal, or an audio signal related to a user's needs in the ambient audio signal. The manner of acquiring the target audio signal of the user may include, but is not limited to: obtaining corresponding audio information when the neuron state of the user meets a preset condition through the neuron state of the user reflected by the acquired brain wave data of the user, wherein the audio information is the audio information concerned by the user; or, the audio information concerned by the user can be obtained by obtaining the behavior log of the user; the behavior log of the user may include interests and hobbies of the user, downloading history information of the user, browsing history information of the user, and the like.

It should be noted that the environmental audio signal may refer to audio information in an environment where a user perceives when the noise reduction device is used and the noise reduction function is not turned on; the initial reference signal may refer to an audio signal collected by a device in the noise reduction apparatus; for example, it may refer to an audio signal collected by a microphone of the noise reduction apparatus. It should be understood that the ambient audio signal may differ from the audio information included in the initial reference signal. Any device or device that assists in the noise reduction function may be considered part of the noise reduction apparatus.

In the method, a target reference signal is obtained by obtaining a target audio signal concerned by a user; wherein the target reference signal does not include a target audio signal of the user; obtaining a noise reduction signal according to the target reference signal and the environment audio signal, wherein the noise reduction signal is used for offsetting the target reference signal; the noise reduction signal after the active noise reduction processing still comprises target audio information concerned by the user, so that the active noise reduction method can meet the personalized requirements of the user.

With reference to the first aspect, in certain implementations of the first aspect, the acquiring a target audio signal of a user includes:

acquiring brain wave data of the user;

and acquiring a target audio signal of the user according to the brain wave data of the user.

In the embodiment of the application, the target audio signal concerned by the user can be acquired according to the acquired brain wave data of the user on the environment audio signal.

It should be understood that the above brain wave data may also be referred to as brain wave data, which refers to data of the brain of the user obtained by a method of recording brain activity using electrophysiological indicators. When the brain of a user is active, postsynaptic potentials synchronously generated by a large number of neurons are summed up to form brain waves, and the brain waves record the electric wave change during the brain activity and are the overall reflection of the electrophysiological activity of brain nerve cells on the surface of the cerebral cortex or scalp. The brain wave data may be different for different users.

With reference to the first aspect, in certain implementations of the first aspect, the electroencephalogram data is used to reflect a neuron state of the user when the user acquires the environmental audio signal, and the target audio signal is an audio signal in the environmental audio signal, where the neuron state of the user satisfies a preset condition.

For example, the preset condition may include that the fluctuation range of the neuron state of the user satisfies a preset threshold, or other preconfigured conditions; for example, an audio signal that causes a neuron state to fluctuate greatly may be a target audio signal.

With reference to the first aspect, in certain implementations of the first aspect, the target audio signal refers to an audio signal that is preconfigured according to a behavior log of the user.

Illustratively, the user's behavioral log interests, the user's download history information, the user's browsing history information, and the like.

In one possible implementation, the target audio signal of the user may be obtained according to the pre-configured user information. For example, the target audio signal may refer to pre-configured text information focused by the user, or voice information.

With reference to the first aspect, in certain implementations of the first aspect, the acquiring a target audio signal of the user from brain wave data of the user includes:

converting the brain wave data of the user into sound channel motion information, wherein the sound channel motion information is used for representing the motion information of a sound channel occlusion part when the user speaks;

and obtaining the target audio signal according to the sound channel motion information.

It should be noted that, in order to realize the conversion and mapping of brain wave data to vocal tract motion information, a large amount of vocal tract motion information during human speaking may be associated with brain wave data (e.g., neural activity data) of a user; wherein, the vocal tract running information can comprise the motion information of lips, tongue, larynx and mandible; the pre-trained recurrent neural network can be obtained through the training data to obtain the audio information corresponding to the sound channel motion information; the training data may include sample channel motion information and sample audio information, and the recurrent neural network is used to establish an association relationship between the channel running information and the audio information.

With reference to the first aspect, in certain implementations of the first aspect, the target audio signal is a target audio signal at a current time, and further includes:

and predicting the target audio signal at the next moment of the current moment according to the target audio signal at the current moment.

It should be understood that the current time and the time next to the current time may be consecutive times, or may refer to non-consecutive times; for example, the next instant in time to the current instant in time may differ from the current instant in time by a periodic time interval, which may include time units of milliseconds or microseconds.

In the embodiment of the application, as the target audio signal of the user is acquired through the brain wave data, part of real-time service requirements may not be met; therefore, the target audio signal of the user can also be obtained by adopting a prediction model; the prediction model is used for predicting a target audio signal of the user at a future moment based on the brain wave data of the user at a historical moment or a current moment and the target audio signal of the user.

With reference to the first aspect, in certain implementations of the first aspect, the obtaining a target reference signal according to the target audio signal and an initial reference signal includes:

and filtering the initial reference signal according to the target audio signal to obtain the target reference signal.

With reference to the first aspect, in certain implementations of the first aspect, the filtering the initial reference signal according to the target audio signal includes:

and filtering the initial reference signal by adopting a filter according to the target audio signal.

In one possible implementation, the filter may include an adaptive filter, or other filters.

With reference to the first aspect, in certain implementations of the first aspect, the acquiring the ambient audio signal includes:

receiving the environment audio signal from a cooperative device, wherein the cooperative device is used for acquiring the environment audio signal from a sound source.

In the embodiment of the application, the collected environmental audio signals are forwarded by adopting the cooperative equipment, so that the user can perceive the environmental audio signals before the environmental audio signals reach the user; thereby enabling more efficient acquisition of a target audio signal of interest to the user.

In a possible implementation manner, the cooperative device may send the environmental audio signal to the noise reduction apparatus, and before the environmental audio signal is played on the noise reduction apparatus, the noise reduction apparatus may perform scaling processing on the energy level of the environmental audio signal, so that the played sound does not affect the normal perception of the user; for triggering an advanced perception of audio information of interest by the user, resulting in such audio perception before arrival of the direct sound waves, and for rendering of the target audio information based on the brain wave data.

In a possible implementation manner, the cooperative device may also perform scaling processing on the acquired environmental audio signal and then forward the processed environmental audio signal to the noise reduction apparatus.

In a second aspect, an active noise reduction device is provided, comprising:

the device comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring a target audio signal, an environment audio signal and an initial reference signal of a user, the target audio signal is used for representing audio information concerned by the user, the environment audio signal is used for representing audio information of the environment where the user is located, and the initial reference signal is used for carrying out active noise reduction processing on the environment audio signal; the processing module is used for obtaining a target reference signal according to the target audio signal and the initial reference signal, wherein the target reference signal does not include the target audio signal; and obtaining a noise reduction signal according to the target reference signal and the environment audio signal, wherein the noise reduction signal is used for offsetting the target reference signal.

With reference to the second aspect, in some implementations of the second aspect, the obtaining module is specifically configured to:

acquiring brain wave data of the user;

the processing module is specifically configured to:

With reference to the second aspect, in certain implementations of the second aspect, the electroencephalogram data is used to reflect a neuron state of the user when the user acquires the environmental audio signal, and the target audio signal is an audio signal in the environmental audio signal, where the neuron state of the user satisfies a preset condition.

With reference to the second aspect, in some implementations of the second aspect, the target audio signal refers to an audio signal that is pre-configured according to a behavior log of the user.

With reference to the second aspect, in some implementations of the second aspect, the processing module is specifically configured to:

With reference to the second aspect, in certain implementations of the second aspect, the target audio signal is a target audio signal at a current time, and the processing module is further configured to:

and filtering the initial reference signal by adopting a self-adaptive filter according to the target audio signal.

receiving the environmental audio signal from a cooperative device, the cooperative device being configured to acquire the environmental audio signal from a sound source.

In a third aspect, an active noise reduction apparatus is provided, which includes a memory for storing a program; a processor for executing the memory-stored program, the processor for performing, when the memory-stored program is executed: acquiring a target audio signal, an environment audio signal and an initial reference signal of a user, wherein the target audio signal is used for representing audio information concerned by the user, the environment audio signal is used for representing audio information of an environment where the user is located, and the initial reference signal is used for carrying out active noise reduction processing on the environment audio signal; obtaining a target reference signal according to the target audio signal and the initial reference signal, wherein the target reference signal does not include the target audio signal; and obtaining a noise reduction signal according to the target reference signal and the environment audio signal, wherein the noise reduction signal is used for offsetting the target reference signal.

In a possible implementation manner, the active noise reduction apparatus includes a processor, and is further configured to execute the active noise reduction method in any one implementation manner of the first aspect and the first aspect.

It will be appreciated that extensions, definitions, explanations and explanations of relevant content in the above-described first aspect also apply to the same content in the third aspect.

In a fourth aspect, an active noise reduction headphone is provided, configured to perform the active noise reduction method in the first aspect and any implementation manner of the first aspect.

It is to be understood that extensions, definitions, explanations and explanations of relevant contents in the above-described first aspect also apply to the same contents in the fourth aspect.

In a fifth aspect, a vehicle headrest apparatus is provided, which is used to perform the active noise reduction method in the first aspect and any implementation manner of the first aspect.

It will be appreciated that extensions, definitions, explanations and explanations of relevant content in the above-described first aspect also apply to the same content in the fifth aspect.

In a sixth aspect, an automobile is provided, which includes an active noise reduction device according to the second aspect.

It will be appreciated that extensions, definitions, explanations and explanations of relevant content in the above-described first aspect also apply to the same content in the sixth aspect.

In a seventh aspect, an active noise reduction system is provided, which is used to implement the active noise reduction apparatus in any implementation manner of the second aspect and the second aspect.

It will be appreciated that extensions, definitions, explanations and explanations of relevant content in the above-described first aspect also apply to the same content in the seventh aspect.

In an eighth aspect, a computer program product containing instructions is provided, which when run on a computer causes the computer to perform the active noise reduction method in the first aspect and any one of the implementations of the first aspect.

In a ninth aspect, a chip is provided, where the chip includes a processor and a data interface, and the processor reads an instruction stored in a memory through the data interface, and executes the active noise reduction method in any one implementation manner of the first aspect and the first aspect.

Optionally, as an implementation manner, the chip may further include a memory, where the memory stores instructions, and the processor is configured to execute the instructions stored on the memory, and when the instructions are executed, the processor is configured to execute the active noise reduction method in any one implementation manner of the first aspect and the first aspect.

Drawings

FIG. 1 is a schematic block diagram of an active noise reduction system;

FIG. 2 is a schematic diagram of an active noise reduction system;

FIG. 3 is a schematic diagram illustrating superposition cancellation of a noise reduction signal and a noise signal;

FIG. 4 is a schematic diagram of the basic principles of an active noise reduction system;

FIG. 5 is a schematic flow chart of an active noise reduction method provided by an embodiment of the present application;

FIG. 6 is a diagram illustrating an architecture of an active noise reduction method according to an embodiment of the present disclosure;

FIG. 7 is a schematic diagram of acquiring an environmental audio signal by a cooperative device according to an embodiment of the present application;

FIG. 8 is a schematic diagram of a method of acquiring a target audio signal based on brain wave data of a user;

fig. 9 is a schematic diagram of a filtering process performed on an audio signal;

FIG. 10 is a schematic block diagram of an active noise reduction apparatus provided by an embodiment of the present application;

FIG. 11 is a schematic block diagram of an active noise reduction apparatus provided by an embodiment of the present application;

fig. 12 is a schematic hardware structure diagram of an active noise reduction device according to an embodiment of the present application.

Detailed Description

The technical solution in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application; it is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

First, the concepts related to the embodiments of the present application will be briefly described.

Active Noise Cancellation (ANC) is a technology for removing noise by canceling sound waves based on the principle of sound wave superposition; active noise reduction systems may include both feedforward and feedback types.

As an example, the composition and noise reduction principle of the active noise reduction system will be described in detail below with reference to fig. 1, fig. 2, fig. 3, and fig. 4.

Illustratively, as shown in FIG. 1, the active noise reduction system 100 may generally include a controller 110, a speaker (spaker) 120, an error sensor (error mic)130, and a reference sensor (ref mic) 140.

Referring to fig. 2, the working principle and the working flow of the active noise reduction system shown in fig. 1 will be described in detail.

The method comprises the following steps: error sensor 130 collects error signal e (n) and passes error signal e (n) to controller 110.

Wherein, the error signal e (n) is used to represent the sound field characteristics in the quiet zone shown in fig. 2; for example, the sound field characteristic includes a sound pressure characteristic. The concept of dead zones will be described below and will not be described in detail here.

Error sensor 130 may include an acoustic sensor, an optical sensor, or other sensor; the error sensor 130 may acquire audio information by collecting vibration. For example, as shown in fig. 2 and 3, the error sensor 130 may include a microphone.

Step two: the reference sensor 140 collects the noise signal x (n) and passes the noise signal x (n) to the controller 110.

For example, the noise signal x (n) may refer to an initial reference signal used for performing active noise reduction on the environmental audio signal in this application, that is, in the embodiment of this application, the noise signal x (n) acquired by the reference sensor may be used for performing active noise reduction on the environmental audio signal.

It should be understood that the noise signal x (n) collected by the reference sensor 140 is an ambient noise signal. The ambient noise signal is typically emitted by an undesired noise source, as shown in fig. 2.

The reference sensor 140 is typically an acoustic sensor. As shown in fig. 2 and 3, the reference sensor 140 is a microphone.

Step three: controller 110 calculates an error cost function based on error signal e (n) and predicts a noise reduction signal y (n) output by speaker 120 based on noise signal x (n) based on an error cost function minimization principle.

The noise reduction signal y (n) is used to cancel the noise signal x (n). Ideally, the noise reduction signal y (n) is an inverse of the noise signal x (n), i.e. the noise reduction signal y (n) is added to the noise signal x (n) to be zero. The noise reduction signal y (n) may also be referred to as the anti-noise signal.

For example, the controller 110 may be an adaptive filter.

Step IV: the speaker 120 outputs a noise reduction signal y (n) according to the control of the controller 110; for example, the controller 110 calculates an error cost function of the superimposed signal e (n) after reaching the dead zone; and controls the output signal of the speaker 120 based on the principle of error cost function minimization. Alternatively, the speaker 120 controls the noise reduction signal y (n) output from the speaker according to the inverse signal for canceling the noise generated by the controller 110. As shown in fig. 2, the noise signal x (n) and the noise reduction signal y (n) reach the quiet zone through the primary path and the secondary path, respectively.

As shown in fig. 3, the error sensor 130 collects a sound signal obtained by superimposing the noise signal x (n) and the noise reduction signal y (n) after passing through the primary path and the secondary path, respectively, and reaching the quiet zone, and the sound signal is referred to as an error signal e (n). The noise signal e (n) collected by the error sensor 130 can also be described as residual noise after the noise reduction process.

The goal of the controller 110 to predict the noise reduction signal y (n) output by the speaker 120 is to minimize the error cost function of the noise signal x (n) and the noise reduction signal y (n) after they reach the dead zone via the primary path and the secondary path, respectively, and then are superimposed on each other.

For example, if the noise source is considered a primary sound source, the speaker 120 may be referred to as a secondary sound source, as shown in fig. 2.

It should be understood that the effect of the superposition of the noise signal and the noise reduction signal at different locations is not necessarily the same. It is assumed that the error sensor collects an error signal at point a, which may characterize the effect of the noise signal on the noise reduction signal at point a, but may not necessarily characterize the effect of the noise signal on the noise reduction signal at a location other than point a. In order to express which region the error signal of the active noise reduction represents the active noise reduction effect, a concept of a dead zone (quiet zone) is proposed, which indicates a region or a space where the error signal acquired by the error sensor is located. That is, where the error sensor collects the signal, where is the dead band. For example, in fig. 2, the dead zone indicates an area where the error signal e (n) acquired by the error sensor 130 is located.

It should also be appreciated that the goal of the controller 110 to predict the noise reduction signal y (n) output by the speaker 120 is to minimize the error cost function of the noise reduction signal y (n) and the noise signal x (n) after they reach the dead band, respectively, and then add up to the signal e (n). That is, the active noise reduction system aims to achieve an active noise reduction effect in the dead zone.

Illustratively, FIG. 4 is a schematic diagram of the basic principles of an active noise reduction system.

The active noise reduction system may be a comprehensive active noise reduction system, which is a combination of a feedforward type and a feedback type. d can refer to signal source signal including useful signal S and noise interference n₀Summing; x may refer to a reference signal, and the reference signal may be x ═ n₁，n ₁Refers to interference with noise n₀The correlated signal. Suppose S, n₀、n ₁Is a smooth random process with zero mean and satisfies S and n₀、n ₁Not correlated with each other, the output signal y of the filter being n₂，n ₂Can represent the noise n₁The output of the whole system is:

Z＝d-y＝S+n ₀-y；

the equation is squared on both sides:

Z ²＝d ²-y ²＝S ²+(n ₀-y) ²+2S(n ₀-y)；

the equation takes the expected values on both sides:

wherein, E [ Z²]May represent the power of the signal; from the above, it can be seen that to make the system output Z maximally close to the signal S, we are making e [ (n)₀-y) ²]Taking the minimum value; and Z-S ═ n₀-y, in the ideal case, y ═ n₀If Z is S; the noise of the output signal Z is completely cancelled and only the useful signal S remains.

Thus, it can be seen that the essence of the active noise reduction system is: signals including x (n) in d (n) or related to x (n) are removed, and if the content of x (n) is substantially identical to that of d (n), the signals of d (n) are cancelled. .

In the active noise reduction system, noise reduction processing is basically performed on an environmental audio signal d (n), which may also be referred to as a signal source signal, based on a noise signal x (n) acquired by a reference sensor, so that the noise reduction signal after the noise reduction processing is played to a user. In the existing active noise reduction system, the collected noise signals x (n) in the environment are not distinguished, that is, the noise signals x (n) are fully reduced to the maximum extent, and any external sound signals are suppressed. However, in daily life, users still want to effectively sense their audio information of interest, such as audio signals of interest to the users or real-time audio signals related to needs; therefore, the current active noise reduction method cannot meet the personalized requirements of the user, that is, cannot perform selective noise reduction processing according to the requirements of the user.

In view of this, the present application provides an active noise reduction method and an active noise reduction apparatus, which obtain a target reference signal by obtaining a target audio signal concerned by a user; wherein the target reference signal does not include a target audio signal of the user; obtaining a noise reduction signal according to the target reference signal and the environment audio signal, wherein the noise reduction signal is used for offsetting the target reference signal; the noise reduction signals after the active noise reduction processing still include target audio signals concerned by the user, so that the active noise reduction method can meet the personalized requirements of the user.

The technical solution in the present application will be described in detail with reference to fig. 5 to 9.

Firstly, it should be noted that the active noise reduction method of the present application can be applied to a noise reduction device, where the noise reduction device refers to a device having a noise reduction requirement; for example, the noise reduction device may include a noise reduction playing device and a user headrest device in an in-vehicle scene.

Illustratively, the noise reduction playing device may include an active noise reduction headphone, an active noise reduction headband device, or the like; a user headrest device in an in-vehicle scenario may refer to a device used for sound playback at a seat headrest in an automobile.

Fig. 5 is a schematic flowchart of an active noise reduction method according to an embodiment of the present application. For example, the method 200 is performed by any of the noise reduction devices described above. The method 200 includes steps S210 to S230, and the steps S210 to S230 are described in detail below.

S210, acquiring a target audio signal, an environment audio signal and an initial reference signal of a user.

The target audio signal is used for representing audio information concerned by the user, the environment audio signal is used for representing audio information of the environment where the user is located, and the initial reference signal is used for carrying out active noise reduction processing on the environment audio signal.

It should be understood that the target audio signal may refer to an audio signal of interest to the user; for example, the target audio signal may refer to an audio signal of interest to a user in the ambient audio signal, or an audio signal related to a user's needs in the ambient audio signal. For example, the target audio signal may be target audio information that may be focused by the user by acquiring brain wave data of the user and reflecting a neuron state of the user according to the brain wave data; alternatively, the target audio signal may also be audio information configured in advance according to a behavior log of the user; the user's behavior log may include the user's hobbies, the user's download history information, and the like.

It should be noted that the environmental audio signal may refer to audio information in an environment where a user perceives when the noise reduction device is used and the noise reduction function is not turned on; the initial reference signal may refer to an audio signal collected by a device in the noise reduction apparatus; for example, it may refer to an audio signal collected by a microphone of the noise reduction apparatus. It is to be understood that the ambient audio signal may differ from the audio information comprised in the initial reference signal. Any device or device that assists in the noise reduction function may be considered part of the noise reduction apparatus.

Optionally, in a possible implementation manner, acquiring a target audio signal of a user includes: acquiring brain wave data of the user; and acquiring a target audio signal of the user according to the brain wave data of the user.

Optionally, in a possible implementation manner, the electroencephalogram data is used to reflect a neuron state of the user acquiring the environmental audio signal, and the target audio signal is an audio signal of the environmental audio signal in which the neuron state of the user satisfies a preset condition.

The preset condition may include that a fluctuation range of the neuron state of the user meets a preset threshold, or other preset conditions; for example, an audio signal that causes a neuron state to fluctuate greatly may be a target audio signal.

Illustratively, brain wave data of the user can be converted into sound channel motion information, and the sound channel motion information is used for representing motion information of a sound channel occlusion part when the user speaks; and obtaining the target audio signal according to the sound channel motion information.

In the embodiment of the application, the target audio signal concerned by the user can be acquired according to the brain wave data of the user. The specific flow can be seen in the following schematic diagram of the method for acquiring the target audio information based on the user brain wave data shown in fig. 8.

Optionally, in a possible implementation manner, the target audio signal refers to an audio signal that is configured in advance according to the behavior log of the user.

Illustratively, the user's behavior log includes the user's hobbies, the user's download history information, and the like.

Optionally, in a possible implementation manner, the target audio signal is a target audio signal at a current time, and further includes:

and predicting a target audio signal at the next moment of the current moment according to the target audio signal at the current moment.

In the embodiment of the application, as the target audio signal of the user is acquired through the brain wave data, part of real-time service requirements may not be met; therefore, the target audio information of the user can be obtained by adopting a prediction model; the prediction model is used for predicting target audio information of the user at a future moment based on the brain wave data of the user at a historical moment or a current moment and the target audio information of the user.

It should be understood that the current time and the next time of the current time may be consecutive times, or may also refer to discontinuous times; for example, the next instant in time to the current instant in time may differ from the current instant in time by a periodic time interval, which may include time units of milliseconds or microseconds.

Optionally, in one possible implementation, acquiring an ambient audio signal includes:

an ambient audio signal is received from a cooperating device, the cooperating device being arranged to acquire the ambient audio signal from a sound source.

In the embodiment of the application, the collected environmental audio signals are forwarded by adopting the cooperative equipment, so that the user can perceive the environmental audio signals before the environmental audio signals reach the user; thereby, the target audio information concerned by the user can be more efficiently acquired. The specific process can be seen in the following fig. 7.

And S220, obtaining a target reference signal according to the target audio signal and the initial reference signal.

Wherein the target audio signal is not included in the target reference signal.

Optionally, in a possible implementation manner, obtaining the target reference signal according to the target audio signal and the initial reference signal includes:

and filtering the initial reference signal according to the target audio signal to obtain a target reference signal.

It should be noted that the initial reference signal is used to represent a noise signal for performing active noise reduction processing on the environmental audio signal, that is, the initial reference signal includes an audio signal that is of interest to the user and an audio signal that is not of interest to the user; by filtering the initial reference signal, the audio signal which is not concerned by the user can be filtered out from the obtained target reference signal.

Optionally, in a possible implementation manner, the filtering processing on the initial reference signal according to the target audio signal includes:

and filtering the initial reference signal by adopting a filter according to the target audio signal. Wherein the filter may comprise an adaptive filter, or other filters; the specific process can be seen in the following fig. 9.

And S230, obtaining a noise reduction signal according to the target reference signal and the environment audio signal.

Wherein the noise reduction signal is used to cancel the target reference signal. For example, the noise reduction signal may generate a backward wave to cancel the target reference signal.

Further, after step S230, a noise reduction signal may be played to the user.

Exemplarily, fig. 6 is an architecture diagram of an active noise reduction method provided in an embodiment of the present application. The architecture diagram shown in fig. 6 includes the following steps:

the method comprises the following steps: and acquiring target audio information of the user.

The target audio signal of the user may refer to an audio signal of interest to the user, or may refer to an audio signal related to a user requirement.

In one example, a target audio signal of a user may be obtained based on brain wave data of the user.

For example, an audio signal that the user is currently focusing on or interested in may be acquired based on the current brain wave data of the user.

Further, in order to better acquire the target audio information of the user from the brain wave data of the user, the external environment audio signal can be sent to the user more quickly; for example, the collected environmental audio signal is sent to the noise reduction device by using a cooperative device.

Illustratively, as shown in fig. 7, the cooperative device may refer to a device independent from the noise reduction apparatus, for example, the cooperative device may refer to an electronic device having an audio information transfer function; the cooperative equipment ratio can comprise various electronic products such as a mobile phone, a watch and the like.

For example, a cooperating device may be one device or a plurality of devices; the cooperative device may report the location information to the noise reduction apparatus; the timing of reporting the location information may include: the reporting of the location information may be performed when the environmental audio signal is sent to the noise reduction apparatus, or the reporting of the location information may be performed when the cooperative device first establishes a connection with the noise reduction apparatus, or the cooperative device may be set at a default location/a required location.

In the embodiment of the application, the collected environmental audio signals are forwarded by adopting the cooperative equipment, so that the user can perceive the environmental audio signals before the environmental audio signals reach the user; thereby, the target audio information concerned by the user can be more efficiently acquired.

Illustratively, the cooperative device may perform the following substeps:

the first substep: the collaboration device collects an ambient audio signal.

And a second substep: the collaborating device sends an ambient audio signal.

For example, the cooperative apparatus may send the collected ambient audio signal d (n) to the noise reduction device; for example, the environmental audio signal may be transmitted by using a wireless method, such as bluetooth, wifi, 5G, etc. Optionally, in a possible implementation manner, the cooperative device may send the ambient audio signal to the noise reduction apparatus, and before playing the ambient audio signal on the noise reduction apparatus, the noise reduction apparatus may perform scaling processing on an energy level of the ambient audio signal, so that the played sound does not affect the normal perception of the user; for triggering an advanced perception of audio information of interest by the user, such that this audio perception is formed before the arrival of the direct sound waves d (n), and rendering of the target audio information is performed based on the brain wave data.

Optionally, in another possible implementation manner, the cooperative device may also perform scaling processing on the acquired environmental audio signal and then forward the processed environmental audio signal to the noise reduction apparatus.

Step two: an ambient audio signal is collected.

For example, a microphone device in the noise reduction apparatus may be used to capture a noise signal x (n) of an environment where the user is located, where the noise signal x (n) includes an audio signal s (n) that is of interest to the user, and an audio signal n (n) that is not of interest to the user relative to the audio signal s (n).

Step three: and (5) audio signal filtering processing.

And filtering the target audio signal from the noise signal x (n) according to the acquired target audio signal of the user to obtain a target reference signal x' (n).

It should be noted that, the noise signal x (n) includes an audio signal s (n) that is of interest to the user, and an audio signal n (n) that is not of interest to the user relative to the audio signal s (n); after the noise signal x (n) is filtered, the obtained target reference signal x' (n) only includes the audio signal n (n) which is not concerned by the user.

Step four: and carrying out active noise reduction processing.

And carrying out active noise reduction processing on the collected environment audio signal d (n) according to the target reference signal x' (n) to obtain a noise reduction signal.

It should be understood that the ambient audio signal d (n) is an audio signal collected up to the noise reduction means, i.e. both audio signals of interest to the user and audio signals not of interest to the user are included in d (n); and performing active noise reduction processing on the environmental audio signal according to the target reference signal, so that the noise-reduced signal after the noise reduction processing comprises target audio information concerned by the user, and the user can listen selectively.

In one example, the method for acquiring the target audio information of the user in the step one above may refer to fig. 8.

Fig. 8 is a schematic diagram of a method of acquiring a target audio signal based on brain wave data of a user. The method 300 includes steps S310 to S330, and the steps S310 to S330 are described in detail below.

It should be understood that the main body of the method 300 may be a noise reduction device, or may also refer to a brain wave data processing device independent of the noise reduction device, and the brain wave data processing device is used for acquiring brain wave data of the user for analysis and obtaining target audio information focused by the user.

And S310, acquiring brain wave data of the user.

Illustratively, the brain wave data of the user can be acquired by a brain wave acquirer or other brain wave acquisition sensors.

For example, the brain wave data of the user may be collected when the user inputs an account password to log in the medical system, which includes brain wave signal values at different time points, or may be data obtained in real time by a brain wave collector. Of course, for different application scenarios, the brain wave data may also be collected under other actions, which is not limited in this embodiment.

And S320, converting the brain wave data into sound channel motion information.

Illustratively, to achieve the translation and mapping of brain wave data to vocal tract motion information, a large amount of vocal tract motion information when a person speaks may be associated with brain wave data (e.g., neural activity data) of a user; the vocal tract operation information may include motion information of lips, tongue, larynx and mandible, among others.

And S330, obtaining a target audio signal according to the sound channel motion information.

Illustratively, the pre-trained recurrent neural network can be obtained through training data to obtain audio information corresponding to the vocal tract motion information; the training data may include sample channel motion information and sample audio information, and the recurrent neural network is used to establish an association relationship between the channel running information and the audio information.

It should be noted that the method for acquiring the target audio information of the user shown in fig. 8 is for example, other methods in the prior art may also be used to acquire the target audio information of the user through brain wave data, or a method of pre-configuring the target audio information of the user into the noise reduction apparatus may also be used, for example, the target audio signal may refer to pre-configured text information or voice information focused by the user; the embodiment of the present application is not limited to this.

In one example, the method of the audio signal filtering process in step three above can be seen in fig. 9.

For example, an adaptive filtering architecture may be adopted to perform filtering processing, and remove the acquired target audio signal of the user from the noise signal x (n) acquired by direct acquisition, so as to obtain a target reference signal x' (n).

It should be understood that the architecture shown in fig. 9 is similar to the ANC noise reduction algorithm model principle.

For example, the error signal e (n) of the adaptive filter is:

e(n)＝x(n)-x'(n)＝x(n)-s' ^T(n)w(n)＝x(n)-w ^T(n)s'(n)；

wherein, x (n) represents a signal to be filtered, namely an acquired initial reference signal; x' (n) represents a signal output after filtering, i.e., a target reference signal; s' (n) represents a target audio signal of the user, i.e., an audio signal of interest to the user.

By adopting the filtering algorithm, the target audio signal s '(n) concerned by the user in the noise signal x (n) can be filtered out, so that the target reference signal x' (n) not including the target audio signal concerned by the user is obtained.

For example, the method for acquiring the target audio information based on the brain wave data in fig. 8 may be to acquire the target audio information of the user by acquiring the brain wave data of the user in real time and analyzing the brain wave data.

In one example, part of the real-time business requirements may not be met due to the fact that the target audio information of the user is obtained through brain wave data; therefore, in the embodiment of the present application, the target audio information of the user may also be obtained by using a prediction model; the prediction model is used for predicting target audio information of the user at a future moment based on the brain wave data of the user at a historical moment or a current moment and the target audio information of the user.

For example, the prediction model may employ a recurrent neural network, the output of which depends not only on the current input data but also on the state at the previous time; the introduction of the timing feedback mechanism effectively utilizes the context information, and has important significance on the analysis of the timing signals such as voice, text and the like. The input of a recurrent neural network can be composed of two parts, namely the hidden layer state information of the recurrent neural network at the previous moment and the current input information, and the calculation formula is as follows:

h _t＝f(Wx _t+Uh _t-1+b)；

wherein W, U, b is the weight parameter of the model,

in the formula h_tRepresenting the hidden layer state at time t; x is the number of_tAn input representing time t; f represents a nonlinear activation function, and for example, a tanh function, a sigmoid function, a ReLu function, or the like may be used as the activation function.

Illustratively, a recurrent neural network may be used to generate the text task, assuming that the input X ═ X₁,x ₂,x ₃,x ₄]The corresponding output is Y ═ Y₁,y ₂,y ₃,y ₄]The corresponding formula is as follows:

wherein V, c are all parameters,

representing the output of the recurrent neural network at time t.

For example, in a text generation task, the output dimension of the recurrent neural network may be the same size as the number of words in the vocabulary.

When the recurrent neural network is trained, the required language model can be obtained by training with the cross entropy as a loss function, and then the prediction model can predict the target audio information of the user at the next moment based on the target audio information of the user at the historical moment. In executing the prediction task, the output of each time of history can be acquired

And acquiring the word with the highest probability as output.

In the embodiment of the application, voice information can be used, and the voice information can be converted into text information; specifically, the method comprises the following steps:

the method comprises the following steps: converting a target audio signal s' (n) of a user acquired at the current moment into a speech text (n) based on speech recognition;

step two: taking the text ' (n) as the input of a recurrent neural network, and then obtaining an output text ' (n) based on the recurrent neural network trained in advance, wherein the text ' (n) can be used for representing the target audio information of the user at the next moment of the predicted current moment;

step three: carrying out voice synthesis on the text '(n) to obtain predicted target audio information s' (n) at the next moment of the current moment;

step four: and (5) merging s ' (n) into s ' (n) to obtain target audio information s ' (n) ═ s ' (n) + s ' (n) of the user predicted after supplementation.

Further, the target audio information of the user predicted after the supplementation may be removed from the collected noise signal x (n) by the method shown in fig. 9, so as to obtain a target reference signal x' (n).

In the embodiment of the application, a prediction model is introduced when target audio information of a user is acquired based on brain wave data, namely, audio information concerned by the user is acquired; therefore, the time for acquiring the target audio information of the user can be further reduced, and the audio information concerned by the user can be better known according to the brain wave data of the user and the predicted target audio information of the user, so that the audio information concerned by the user can be better retained in the noise reduction signal, and the user can better select to listen.

In the method, a target reference signal is obtained by obtaining a target audio signal concerned by a user; wherein the target reference signal does not include a target audio signal of the user; obtaining a noise reduction signal according to the target reference signal and the environment audio signal, wherein the noise reduction signal is used for offsetting the target reference signal; the noise reduction signals after the active noise reduction processing still include target audio signals concerned by the user, so that the active noise reduction method can meet the personalized requirements of the user.

It is to be understood that the above description is intended to assist those skilled in the art in understanding the embodiments of the present application and is not intended to limit the embodiments of the present application to the particular values or particular scenarios illustrated. It will be apparent to those skilled in the art from the foregoing description that various equivalent modifications or changes may be made, and such modifications or changes are intended to fall within the scope of the embodiments of the present application.

The active noise reduction method provided by the embodiment of the present application is described in detail above with reference to fig. 1 to 9; the device embodiments of the present application will be described in detail below with reference to fig. 10 to 12. It should be understood that the active noise reduction device in the embodiment of the present application may perform various active noise reduction methods in the embodiments of the present application, that is, specific working processes of various products below, and reference may be made to corresponding processes in the embodiments of the foregoing methods.

FIG. 10 is a schematic block diagram of an active noise reduction apparatus provided herein.

It should be understood that the active noise reduction apparatus 400 may perform the active noise reduction method shown in fig. 5-9. The active noise reduction device 400 includes: an acquisition module 410 and a processing module 420.

The obtaining module 410 is configured to obtain a target audio signal of a user, an environment audio signal, and an initial reference signal, where the target audio signal is used to represent audio information of interest of the user, the environment audio signal is used to represent audio information of an environment where the user is located, and the initial reference signal is used to perform active noise reduction on the environment audio signal; the processing module 420 is configured to obtain a target reference signal according to the target audio signal and the initial reference signal, where the target reference signal does not include the target audio signal; and obtaining a noise reduction signal according to the target reference signal and the environment audio signal, wherein the noise reduction signal is used for offsetting the target reference signal.

Optionally, as an embodiment, the obtaining module 410 is specifically configured to:

acquiring brain wave data of the user;

the processing module is specifically configured to:

Optionally, as an embodiment, the electroencephalogram data is used to reflect a neuron state of the user acquiring the environmental audio signal, and the target audio signal is an audio signal of the environmental audio signal in which the neuron state of the user satisfies a preset condition.

Optionally, as an embodiment, the target audio signal refers to an audio signal that is configured in advance according to the behavior log of the user.

Optionally, as an embodiment, the processing module 420 is specifically configured to:

Optionally, as an embodiment, the target audio signal is a target audio signal at a current time, and the processing module 420 is further configured to:

In one example, the active noise reduction apparatus 400 may be used to perform all or part of the operations of the method shown in any one of fig. 5-9. For example, the obtaining module may be configured to perform all or part of operations in S210 and S310; the processing module may be configured to perform all or part of operations S220, S230, S320, and S330. The obtaining module 410 may refer to an active noise reduction apparatus, which may be a communication interface or a transceiver; for example, the obtaining module 410 may refer to a microphone in an active noise reduction device, or an interface circuit. The processing module 420 may be a processor or chip with computing capabilities in any active noise reduction device.

It should be noted that the active noise reduction apparatus 400 is embodied in the form of a functional unit. The term "module" herein may be implemented in software and/or hardware, and is not particularly limited thereto.

For example, a "module" may be a software program, a hardware circuit, or a combination of both that implements the functionality described above. The hardware circuitry may include an Application Specific Integrated Circuit (ASIC), an electronic circuit, a processor (e.g., a shared processor, a dedicated processor, or a group of processors) and memory that execute one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that support the described functionality.

Accordingly, the units of the respective examples described in the embodiments of the present application can be realized in electronic hardware, or a combination of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

For example, fig. 11 is a schematic diagram of an active noise reduction device. The acquiring module 410 shown in fig. 10 may include the brain wave data acquiring module 510 and the environmental audio signal acquiring module 520 shown in fig. 11; the processing module 420 may include a filtering processing module 530 and a noise reduction processing module 540.

The electroencephalogram data acquisition module 510 is configured to acquire electroencephalogram data of a user; and obtaining a target audio signal of the user according to the brain wave data. For example, the brain wave data acquisition module 510 may be configured to perform S310 to S330.

The environment audio signal obtaining module 520 is configured to obtain an environment audio signal where a user is located; for example, the ambient audio signal may be collected by a microphone of the active noise reduction device. For example, the ambient audio signal acquisition module 520 may be configured to perform S210.

The filtering processing module 530 is configured to perform filtering processing on a target audio signal of a user from the initial reference signal to obtain a target reference signal. For example, the ambient audio signal acquisition module 520 may be configured to perform S220.

The noise reduction processing module 540 is configured to perform active noise reduction processing on the environmental audio signal according to the target reference signal to obtain a noise reduction signal; the noise reduction signal includes a target audio signal of the user, that is, the user can still hear the audio information focused by the user. For example, the noise reduction processing module 540 may be configured to perform S230.

The active noise reduction apparatus 600 shown in fig. 12 includes a memory 610, a processor 620, a communication interface 630, and a bus 640. The memory 610, the processor 620 and the communication interface 630 are connected to each other through a bus 640.

The memory 610 may be a Read Only Memory (ROM), a static memory device, a dynamic memory device, or a Random Access Memory (RAM). The memory 610 may store a program, and when the program stored in the memory 610 is executed by the processor 620, the processor 620 is configured to perform the steps of the active noise reduction method according to the embodiment of the present application; for example, the respective steps shown in fig. 5 to 9 are performed.

It should be understood that the active noise reduction device shown in the embodiment of the present application may be an active noise reduction earphone, or may be a chip configured in the active noise reduction earphone; alternatively, the active noise reduction device shown in the embodiment of the present application may be a car headrest device, or may be a chip configured in the car headrest device.

The active noise reduction device may be a device having an active noise reduction function, and may include any device known in the art; alternatively, the active noise reduction device may also refer to a chip having an active noise reduction function. The active noise reduction device can comprise a memory and a processor; the memory may be configured to store program code, and the processor may be configured to invoke the program code stored by the memory to implement the corresponding functionality of the computing device. The processor and the memory included in the computing device may be implemented by a chip, and are not particularly limited herein.

For example, the memory may be used to store program instructions related to the active noise reduction method provided in the embodiments of the present application, and the processor may be used to call the program instructions related to the active noise reduction method stored in the memory.

The processor 620 may be a general-purpose Central Processing Unit (CPU), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits, and is configured to execute related programs to implement the active noise reduction method according to the embodiment of the present application.

The processor 620 may also be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the active noise reduction method of the present application may be implemented by integrated logic circuits of hardware in the processor 920 or instructions in the form of software.

The processor 620 may also be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, or discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 610, and the processor 620 reads information in the memory 610, and completes functions required to be executed by modules included in the active noise reduction apparatus shown in fig. 10 or fig. 11 in the application implementation in combination with hardware of the processor, or executes the active noise reduction method shown in fig. 5 to fig. 9 in the method embodiment of the application.

Communication interface 630 enables communication between active noise reducer 600 and other devices or communication networks using transceiver devices such as, but not limited to, transceivers.

Bus 640 may include a path to communicate information between various components of active noise reduction apparatus 600 (e.g., memory 610, processor 620, communication interface 630).

It should be noted that although the active noise reducer 600 described above shows only a memory, a processor, and a communication interface, in a specific implementation, those skilled in the art will appreciate that the active noise reducer 600 may also include other components necessary to achieve proper operation. Meanwhile, it should be understood by those skilled in the art that the active noise reduction apparatus 600 may further include hardware devices for implementing other additional functions according to specific needs. Furthermore, it should be understood by those skilled in the art that the active noise reducer 600 may also include only the components necessary to implement the embodiments of the present application, and not all of the components shown in FIG. 12.

Illustratively, the embodiment of the present application further provides an active noise reduction headphone, which may execute the active noise reduction method in the foregoing method embodiments.

Exemplarily, the embodiment of the present application further provides a vehicle headrest device, which may perform the active noise reduction method in the above method embodiments.

For example, the vehicle-mounted headrest device may be an active noise reduction headrest of an automobile seat, a through hole may be formed in the surface of the headrest body, one or more noise reduction speakers may be installed in the through hole, the noise reduction speakers may be used to output noise reduction signals, and the noise reduction signals may be noise reduction signals obtained according to an active noise reduction method in an embodiment of the present application.

In one example, one or more noise reduction speakers may be mounted in the headrest proximate to the user's ears to facilitate the user receiving noise reduction speakers to output noise reduction signals. In one example, one or more microphones may be further mounted on the headrest body, and the microphones may be used to capture an ambient audio signal or an initial reference signal; the microphones used for acquiring the environmental audio signal or the initial reference signal may be the same microphone or different microphones.

For example, for an automobile, the ambient audio signal may be audio information in the cockpit that a user perceives when using the active noise reduction headrest and not turning on the noise reduction function; the initial reference signal may refer to a noise signal for performing active noise reduction processing on an environmental audio signal of the automobile; the initial reference signal may include one or more of an engine noise signal, a wind noise signal, and a road noise signal of the automobile.

In one example, a microphone for capturing the ambient audio signal or the initial reference signal may be mounted anywhere within the cabin. In one possible implementation, the active noise reduction headrest of the car seat may refer to an active noise reduction headrest mounted to a seat of a car driver; alternatively, it may also refer to an active noise reduction headrest mounted to a passenger seat of an automobile; the specific installation position of the active noise reduction headrest is not limited at all.

Exemplarily, the embodiment of the present application further provides an automobile, which includes the active noise reduction device in the above method embodiment.

For example, the active noise reduction headrest of the above embodiments may be included in an automobile.

Illustratively, the embodiment of the present application further provides an active noise reduction system, which may execute the active noise reduction method in the foregoing method embodiments.

Illustratively, the embodiment of the present application further provides a chip, which includes a transceiver unit and a processing unit. The transceiver unit can be an input/output circuit and a communication interface; the processing unit is a processor or a microprocessor or an integrated circuit integrated on the chip; the chip can execute the active noise reduction method in the method embodiment.

Illustratively, the present application further provides a computer-readable storage medium, on which instructions are stored, and the instructions, when executed, perform the active noise reduction method in the above method embodiments.

Illustratively, the present application further provides a computer program product containing instructions, which when executed, perform the active noise reduction method in the above method embodiments.

It should be understood that the processor in the embodiments of the present application may be a Central Processing Unit (CPU), and the processor may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

It will also be appreciated that the memory in the embodiments of the subject application can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. The non-volatile memory may be a read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an electrically Erasable EPROM (EEPROM), or a flash memory. Volatile memory can be Random Access Memory (RAM), which acts as external cache memory. By way of example, but not limitation, many forms of Random Access Memory (RAM) are available, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct bus RAM (DR RAM).

The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. The procedures or functions according to the embodiments of the present application are wholly or partially generated when the computer instructions or the computer program are loaded or executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more collections of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.

It should be understood that the term "and/or" herein is merely one type of association relationship that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone, wherein A and B can be singular or plural. In addition, the "/" in this document generally indicates that the former and latter associated objects are in an "or" relationship, but may also indicate an "and/or" relationship, which may be understood with particular reference to the former and latter text.

In the present application, "at least one" means one or more, "a plurality" means two or more. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of the singular or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or multiple.

It should be understood that, in the various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

An active noise reduction method, comprising:

acquiring a target audio signal, an environment audio signal and an initial reference signal of a user, wherein the target audio signal is used for representing audio information concerned by the user, the environment audio signal is used for representing audio information of an environment where the user is located, and the initial reference signal is used for carrying out active noise reduction processing on the environment audio signal;

obtaining a target reference signal according to the target audio signal and the initial reference signal, wherein the target reference signal does not include the target audio signal;

and obtaining a noise reduction signal according to the target reference signal and the environment audio signal, wherein the noise reduction signal is used for offsetting the target reference signal.
The method of claim 1, wherein the obtaining a target audio signal of a user comprises:

acquiring brain wave data of the user;

and acquiring a target audio signal of the user according to the brain wave data of the user.
The method according to claim 2, wherein the electroencephalogram data is used for reflecting a state of a neuron of the user acquiring the environmental audio signal, and the target audio signal is an audio signal of the environmental audio signal in which the state of the neuron of the user satisfies a preset condition.
The method of claim 1, wherein the target audio signal is an audio signal that is pre-configured according to a behavior log of the user.
The method of claim 2 or 3, wherein the obtaining the target audio signal of the user from the brain wave data of the user comprises:

converting the brain wave data of the user into sound channel motion information, wherein the sound channel motion information is used for representing the motion information of a sound channel occlusion part when the user speaks;

and obtaining the target audio signal according to the sound channel motion information.
The method of any one of claims 2 to 5, wherein the target audio signal is a target audio signal at a current time, further comprising:

and predicting the target audio signal at the next moment of the current moment according to the target audio signal at the current moment.
The method of any one of claims 1 to 6, wherein obtaining a target reference signal from the target audio signal and an initial reference signal comprises:

and filtering the initial reference signal according to the target audio signal to obtain the target reference signal.
The method of claim 7, wherein the filtering the initial reference signal according to the target audio signal comprises:

and filtering the initial reference signal by adopting a filter according to the target audio signal.
The method of any of claims 1-8, wherein the obtaining the ambient audio signal comprises:

receiving the environmental audio signal from a cooperative device, the cooperative device being configured to acquire the environmental audio signal from a sound source.
An active noise reduction device, comprising:

the device comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring a target audio signal, an environment audio signal and an initial reference signal of a user, the target audio signal is used for representing audio information concerned by the user, the environment audio signal is used for representing audio information of the environment where the user is located, and the initial reference signal is used for carrying out active noise reduction processing on the environment audio signal;

the processing module is used for obtaining a target reference signal according to the target audio signal and the initial reference signal, wherein the target reference signal does not include the target audio signal; and obtaining a noise reduction signal according to the target reference signal and the environment audio signal, wherein the noise reduction signal is used for offsetting the target reference signal.
The apparatus of claim 10, wherein the acquisition module is specifically configured to:

acquiring brain wave data of the user;

the processing module is specifically configured to:

and acquiring a target audio signal of the user according to the brain wave data of the user.
The apparatus according to claim 11, wherein the electroencephalogram data is used to reflect a state of a neuron of the user acquiring the environmental audio signal, and the target audio signal is an audio signal of the environmental audio signal in which the state of the neuron of the user satisfies a preset condition.
The apparatus of claim 10, wherein the target audio signal is an audio signal that is pre-configured according to a behavior log of the user.
The apparatus of claim 11 or 12, wherein the processing module is specifically configured to:

converting the brain wave data of the user into sound channel motion information, wherein the sound channel motion information is used for representing the motion information of a sound channel occlusion part when the user speaks;

and obtaining the target audio signal according to the sound channel motion information.
The apparatus of any of claims 10-14, wherein the target audio signal is a target audio signal at a current time, the processing module further configured to:

and predicting the target audio signal at the next moment of the current moment according to the target audio signal at the current moment.
The apparatus according to any one of claims 10 to 15, wherein the processing module is specifically configured to:

and filtering the initial reference signal according to the target audio signal to obtain the target reference signal.
The apparatus of claim 16, wherein the processing module is specifically configured to:

and filtering the initial reference signal by adopting a filter according to the target audio signal.
The apparatus according to any one of claims 10 to 17, wherein the obtaining module is specifically configured to:

receiving the environment audio signal from a cooperative device, wherein the cooperative device is used for acquiring the environment audio signal from a sound source.
An active noise reducing headphone, characterized by being configured to perform the method according to any one of claims 1 to 9.
Headrest arrangement for a vehicle, characterized by being used to carry out a method according to any one of claims 1 to 9.
An automobile, characterized in that it comprises an active noise reduction device according to any one of claims 10 to 18.
An active noise reduction system comprising an active noise reduction device according to any of claims 10 to 18.
An active noise reduction device, comprising:

a memory for storing a computer program;

a processor for executing a computer program stored in the memory to cause the active noise reduction apparatus to perform the method according to any one of claims 1 to 9.
A computer-readable storage medium, in which program instructions are stored, which, when executed by a processor, implement the method of any one of claims 1 to 9.
A chip comprising a processor and a data interface, the processor reading instructions stored on a memory through the data interface to perform the method of any one of claims 1 to 9.