CN113035168B - Self-adaptive noise reduction method and device - Google Patents
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- G10K11/17813—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase characterised by the analysis of input or output signals, e.g. frequency range, modes, transfer functions characterised by the analysis of the acoustic paths, e.g. estimating, calibrating or testing of transfer functions or cross-terms
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Abstract
The application discloses a self-adaptive noise reduction method and device. The specific implementation scheme is as follows: the method comprises the following steps: acquiring a reference noise signal and an error noise signal, and determining that an internal noise signal exists in the error noise signal when the error noise signal is detected to be increased and the reference noise signal is not increased; performing signal component separation on the error noise signal to obtain an internal noise signal and an external residual noise signal; updating the internal noise filtering parameters by using the internal noise signals, and filtering the internal noise signals by using the updated internal noise filtering parameters; updating an external noise filtering parameter by using the external residual noise signal, and filtering the reference noise signal by using the updated external noise filtering parameter; and superposing the filtered reference noise signal and the filtered internal noise signal to generate inverted noise so as to offset external noise. Internal noise is effectively identified, filtering parameters of the filter are accurately estimated, and noise elimination performance is improved.
Description
Technical Field
The present application relates to the field of semiconductors, and more particularly to the field of noise reduction.
Background
In the existing active noise reduction system (ANC: active Noise Control), external noise is first picked up by a reference microphone as a reference noise signal. The reference noise signal is filtered by the ANC filter to generate inverted noise to offset external noise, the external noise which is not offset is taken as external residual noise to be picked up by the error microphone, and then the coefficient of the ANC filter is updated by the self-adaptive controller, so that the influence of the external noise in the earphone is better offset by the inverted noise.
In practical applications, due to the directionality of the microphone and the shielding of the earphone, external noise and burst noise in part of directions, such as noise when a user wears and moves the earphone, cannot be picked up by the reference microphone, but can be picked up by the error microphone, and the external noise picked up by the reference microphone is independent and uncorrelated with the external noise, which is generally called internal noise.
The internal noise is mixed with the residual noise to be used as error noise, and an error noise signal is obtained after the internal noise is picked up by an error microphone. Because the external residual noise signal obtained after the uncombusted external noise is picked up by the error microphone and the internal noise signal obtained after the internal noise is picked up by the error microphone can not be identified by the existing active noise reduction system, the error judgment of the self-adaptive error can be caused, the false self-adaptive estimation is triggered, the self-adaptive filter deviates from the optimal parameter, and even the noise adding effect is caused.
Disclosure of Invention
The embodiment of the application provides a self-adaptive noise reduction method and device, which are used for solving the problems of the related technology, and the technical scheme is as follows:
in a first aspect, an embodiment of the present application provides an adaptive noise reduction method, including:
acquiring a reference noise signal and an error noise signal, and determining that an internal noise signal exists in the error noise signal when the error noise signal is detected to be increased and the reference noise signal is not increased;
performing signal component separation on the error noise signal to obtain an internal noise signal and an external residual noise signal;
updating the internal noise filtering parameters by using the internal noise signals, and filtering the internal noise signals by using the updated internal noise filtering parameters;
updating an external noise filtering parameter by using the external residual noise signal, and filtering the reference noise signal by using the updated external noise filtering parameter;
and superposing the filtered reference noise signal and the filtered internal noise signal to generate inverted noise so as to offset external noise.
In one embodiment, the step of detecting the error noise signal includes at least one of:
calculating an intensity value T1 corresponding to an error noise signal e1 (T) at the current time T, and increasing the error noise signal when the intensity value is larger than a first preset value;
Calculating the difference value between the error noise signal e1 (t) at the current time t and the error noise signal e1 (t-1) at the previous time t-1, and increasing the error noise signal when the difference value is larger than a second preset value;
and calculating the difference value between the intensity value T3 corresponding to the error noise signal e1 (T2-T1) in the current time period T2-T1 and the intensity value T4 corresponding to the error noise signal e1 (T1-T0) in the previous time period T1-T0, and increasing the error noise signal when the difference value is larger than a third preset value.
In one embodiment, the method further comprises:
performing feature analysis on the reference noise signal to obtain a reference feature parameter S ref Reference to characteristic parameter S ref Comprising N reference eigenvalues and N sets of vectors U i And V i (i=1, 2, … N), N being the number of reference microphones for acquiring the reference noise signal.
In one embodiment, the step of detecting the reference noise signal comprises:
comparing N reference characteristic values corresponding to the t-1 moment and N reference characteristic values corresponding to the t moment with a fifth preset value successively to obtain the number N of characteristic values larger than the fifth preset value ref (t-1) and N ref (t)；
At N ref (t) is less than or equal to N ref In the case of (t-1), the reference noise signal is not increased.
In one embodiment, the method further comprises:
performing feature analysis on the error noise signal to obtain an error feature parameter S err Error characteristic parameter S err Comprising M error characteristic values and M groups of vectors U j And V j (j=1, 2, … M), M being the number of error microphones for obtaining the error noise signal, M error eigenvalues forming set D err 。
In one embodiment, the step of determining the presence of an internal noise signal in the error noise signal comprises:
sequentially comparing the M error characteristic values with a sixth preset value to obtain the number M of error characteristic values larger than the sixth preset value inner ，M inner Error feature value and corresponding vector U inner And V inner Characteristic parameters S constituting an internal noise signal inner ；
M-M inner Error feature value and corresponding vector U outer And V outer Characteristic parameter S constituting an external residual noise signal outer ；
Wherein M is inner The error characteristic values form a set D inner ，M-M inner The error characteristic values form a set D outer 。
In one embodiment, the signal component separation of the error noise signal to obtain an internal noise signal and an external residual noise signal comprises:
the internal noise signal e is calculated using the following formula inner And an external residual noise signal e outer
In one embodiment, the method further comprises:
in the case where no increase in the error noise signal is detected or it is determined that no internal noise signal is present in the error noise signal, the error noise signal is taken as an external residual noise signal.
In a second aspect, an embodiment of the present application provides an adaptive noise reduction apparatus, including:
the internal noise signal confirmation module is used for acquiring a reference noise signal and an error noise signal, and determining that the internal noise signal exists in the error noise signal under the condition that the error noise signal is detected to be increased and the reference noise signal is not increased;
the error noise signal separation module is used for separating signal components of the error noise signal to obtain an internal noise signal and an external residual noise signal;
the first filtering module is used for updating the internal noise filtering parameters by using the internal noise signals and filtering the internal noise signals by using the updated internal noise filtering parameters;
the second filtering module is used for updating external noise filtering parameters by using the external residual noise signals and filtering the reference noise signals by using the updated external noise filtering parameters;
and the inverse noise generation module is used for superposing the filtered reference noise signal and the filtered internal noise signal to generate inverse noise so as to offset external noise.
In one embodiment, the internal noise signal confirmation module includes at least one of:
The first error noise detection submodule is used for calculating an intensity value T1 corresponding to an error noise signal e1 (T) at the current moment T, and the error noise signal is increased when the intensity value is larger than a first preset value;
the second error noise detection sub-module is used for calculating the difference value between the error noise signal e1 (t) at the current moment t and the error noise signal e1 (t-1) at the previous moment t-1, and the error noise signal is increased under the condition that the difference value is larger than a second preset value;
the third error noise detection sub-module is configured to calculate a difference between an intensity value T3 corresponding to the error noise signal e1 (T2-T1) in the current time period T2-T1 and an intensity value T3 corresponding to the error noise signal e1 (T1-T0) in the previous time period T1-T0, where the difference is greater than a third preset value.
In one embodiment, the method further comprises:
the reference noise characteristic analysis module is used for carrying out characteristic analysis on the reference noise signal to obtain a reference characteristic parameter S ref Reference to characteristic parameter S ref Comprising N reference eigenvalues and N sets of vectors U i And V i (i=1, 2, … N), N being the number of reference microphones for acquiring the reference noise signal.
In one embodiment, an internal noise signal confirmation module includes:
The reference noise detection sub-module is used for successively comparing the N reference characteristic values corresponding to the t-1 moment and the N reference characteristic values corresponding to the t moment with a fifth preset value to obtain the number N of characteristic values larger than the fifth preset value ref (t-1) and N ref (t); at N ref (t) is less than or equal to N ref (t-1) In the case of (2), the reference noise signal is not increased.
In one embodiment, the method further comprises:
the error noise characteristic analysis module is used for carrying out characteristic analysis on the error noise signal to obtain an error characteristic parameter S err Error characteristic parameter S err Comprising M error characteristic values and M groups of vectors U j And V j (j=1, 2, … M), M being the number of error microphones for obtaining the error noise signal, M error eigenvalues forming set D err 。
In one embodiment, an internal noise signal confirmation module includes:
an internal noise characteristic parameter calculation sub-module for successively comparing M error characteristic values with a sixth preset value to obtain the number M of error characteristic values larger than the sixth preset value inner ，M inner Error feature value and corresponding vector U inner And V inner Characteristic parameters S constituting an internal noise signal inner ；
Residual noise characteristic parameter calculation sub-module for M-M inner Error feature value and corresponding vector U outer And V outer Characteristic parameter S constituting an external residual noise signal outer The method comprises the steps of carrying out a first treatment on the surface of the Wherein M is inner The error characteristic values form a set D inner ，M-M inner The error characteristic values form a set D outer 。
In one embodiment, an error noise signal separation module includes:
an error noise separation calculation sub-module for calculating an internal noise signal e using the following formula inner And an external residual noise signal e outer
In one embodiment, the method further comprises:
in the case where no increase in the error noise signal is detected or it is determined that no internal noise signal is present in the error noise signal, the error noise signal is taken as an external residual noise signal.
In a third aspect, an electronic device is provided, comprising:
at least one processor; and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of the above.
In a fourth aspect, there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of the above.
One embodiment of the above application has the following advantages or benefits: by detecting the error noise signal and the reference noise signal, under the condition that the error noise signal is detected to be increased and the reference noise signal is not increased, determining that an internal noise signal exists in the error noise signal, performing signal component separation on the error noise signal to obtain an internal noise signal and an external residual noise signal, updating an internal noise filtering parameter by using the internal noise signal, updating an external noise filtering parameter by using the external residual noise signal, filtering the internal noise signal by using the updated internal noise filtering parameter, filtering the reference noise signal by using the updated external noise filtering parameter, superposing the filtered reference noise signal and the filtered internal noise signal, and generating anti-phase noise so as to offset external noise. The external residual noise signal and the internal noise signal are effectively identified, the filtering parameters of the adaptive filter are accurately estimated, and the noise elimination performance is improved.
Other effects of the above alternative will be described below in connection with specific embodiments.
Drawings
The drawings are included to provide a better understanding of the present application and are not to be construed as limiting the application. Wherein:
FIG. 1 is a schematic diagram of an active noise reduction system according to the prior art;
FIG. 2 is a schematic diagram of an adaptive noise reduction method according to an embodiment of the application;
FIG. 3 is a schematic diagram of an adaptive noise reduction system according to an embodiment of the present application;
FIG. 4 is a block diagram of an adaptive noise reduction device according to an embodiment of the present application;
fig. 5 is a block diagram of an electronic device for implementing an adaptive noise reduction method according to an embodiment of the present application.
Detailed Description
Exemplary embodiments of the present application will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present application are included to facilitate understanding, and are to be considered merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 shows a schematic structure of an existing active noise reduction system (ANC: active Noise Control). In order to solve the problem in the prior art that the internal noise signal obtained after the internal noise is picked up by the error microphone interferes with the parameter estimation of the ANC filter, in a specific embodiment, an adaptive noise reduction method is provided, as shown in fig. 2, and includes the following steps:
step S110: acquiring a reference noise signal and an error noise signal, and determining that an internal noise signal exists in the error noise signal when the error noise signal is detected to be increased and the reference noise signal is not increased;
step S120: performing signal component separation on the error noise signal to obtain an internal noise signal and an external residual noise signal;
step S130: updating the internal noise filtering parameters by using the internal noise signals, and filtering the internal noise signals by using the updated internal noise filtering parameters;
step S140: updating an external noise filtering parameter by using the external residual noise signal, and filtering the reference noise signal by using the updated external noise filtering parameter;
step S150: and superposing the filtered reference noise signal and the filtered internal noise signal to generate inverted noise so as to offset external noise.
In one example, as shown in FIG. 3, an ANC architecture diagram including a signal characteristic analyzer and a signal component separator is provided. Multiple Reference Microphones (RM) may be deployed in a particular array on the earphone housing, and multiple Error Microphones (EM) may be deployed in a particular array near the speaker. The external noise is picked up by reference microphones RM 1-N, and N reference microphones output 1 frame of reference noise signal including N reference noise sequences X ref . After each reference microphone picks up external noise, a corresponding reference noise sequence X is generated ref The reference noise sequence is a sampling point in time, and the square of the amplitude of the sampling point is the intensity value of the reference noise signal. The resulting sequence is also different due to the different distances of the individual reference microphones from the sound source. The internal noise and residual noise may be picked up by the error microphones EM 1-M (N may be equal to M or different), and the M error microphones output 1 frame of error noise signals including M error noise sequences X err 。
The error noise tracker is used to detect whether a rapid increase in the error noise signal has occurred over a period of time. Specifically, the error noise tracker may be used to receive the error noise signal output by the error microphone 1 in real time, so as to track the variation of the error noise. When the error noise increases rapidly, the signal characteristic analyzer may be triggered to determine whether the reference noise signal increases, and if the reference noise signal does not increase, then it is determined that an internal noise signal is present in the error noise signal. Specifically, N reference noise sequences X output by N reference microphones ref M error noise sequences X output by M error microphones and input to signal characteristic analyzer err Input to a signal characteristic analyzer.The signal characteristic analyzer maps signals onto a space orthogonal to each other by using a classical PCA (Principal components analysis, principal component analysis) algorithm, obtains a characteristic value and a characteristic vector as characteristic parameters, namely a characteristic value sequence Di (i=1, 2, … N) composed of N characteristic values, corresponds to N sets of singular vectors Ui and Vi (i=1, 2, … N) by sequence, and takes the characteristic value sequence D and the corresponding sets of singular vectors U and V as a set of characteristic parameters S. The number of eigenvalues and the number of eigenvectors are the same as the number of microphones. For example, the signal characteristic analyzer is operative on N reference noise sequences X ref Mixed sequence and M error noise sequences X err The mixed sequences are respectively subjected to characteristic decomposition to obtain characteristic parameters S of the reference noise signals ref (reference characteristic parameter) and characteristic parameter S of error noise signal err (error feature parameter), S ref Comprising a sequence of characteristic values D ref ，S err Comprising a sequence of characteristic values D err . The signal characteristic analyzer refers to the characteristic parameter S at the time t-1 ref Medium characteristic value sequence D ref Comparing with a preset threshold Thd_ref, wherein the threshold Thd_ref is an empirical value in debugging, D ref The number of the exceeding threshold Thd_ref is N ref (t-1). The same principle refers to the characteristic parameter S at the time t ref Medium characteristic value sequence D ref Comparing with a preset threshold Thd_ref, wherein the threshold Thd_ref is an empirical value in debugging, D ref The number of the exceeding threshold Thd_ref is N ref (t). At N ref (t) is less than or equal to N ref In the case of (t-1), the reference noise signal is not increased, and it is determined that the internal noise signal is present in the error noise signal, and otherwise, it is determined that the internal noise signal is not present.
If it is determined that internal noise is present, the signal characteristic analyzer will S err Medium eigenvalue D err The threshold thd_inner_err is an empirical value in debugging compared to a preset threshold thd_inner_err. Set D err The number of the middle exceeding threshold thd_inner_err is N inner . This N inner The feature value set Dinner and the corresponding vector set U inner And V inner The characteristic parameters Sinner and the rest N-Ninner characteristic value sets D of the internal noise are formed outer Corresponding vector set U outer And V outer Characteristic parameters S of the residual noise outer To the signal component separator. Signal component separator utilizing S inner And S is outer Extracting an internal noise signal e inner And an external residual noise signal e outer . It should be noted that, initially, there is no sudden internal noise, only external noise, and when external noise is first collected, the inverse noise is 0, and then the external residual noise is external noise.
External residual noise signal e outer In the input external noise adaptive controller, the reference noise sequence X output by combining RM1 is combined ref To update the external noise filtering parameters. Inputting the updated external noise filtering parameters into an external noise ANC filter, wherein the filter utilizes the updated coefficients to output a reference noise sequence X to RM1 ref Filtering to obtain a filtered reversed phase external noise sequence Y outer As a filtered inverted external noise signal. Internal noise signal e inner On the one hand, the internal noise self-adaptive controller is input to update the internal noise filtering parameter, and on the other hand, the internal noise self-adaptive controller is input to the subtracter and subtracted from the filtered inverse internal noise signal output at the previous moment to generate a new internal noise signal. The updated internal noise filtering parameters are input to an internal noise ANC filter, and the filter uses the updated coefficients to update the new internal noise signal e inner Filtering to obtain a filtered inverted internal noise sequence Y inner . Will invert the external noise sequence Y outer And an inverted internal noise sequence Y inner The superimposed, inverted noise is generated to cancel the external noise. It should be noted that the subtractor outputs Y inner Is an inverse internal noise sequence, then-Y inner Which can be considered as an internal noise sequence containing errors, the subtractor realizes e inner -Y inner Functionally, equal to subtracting the error from the internal noise sequence containing the error, a more accurate internal noise signal is generated.
In this embodiment, by detecting the error noise signal and the reference noise signal, when an increase in the error noise signal is detected and the reference noise signal is not increased, determining that an internal noise signal exists in the error noise signal, performing signal component separation on the error noise signal to obtain the internal noise signal and an external residual noise signal, updating an internal noise filtering parameter by using the internal noise signal, updating an external noise filtering parameter by using the external residual noise signal, filtering the internal noise signal by using the updated internal noise filtering parameter, filtering the reference noise signal by using the updated external noise filtering parameter, and superposing the filtered reference noise signal and the filtered internal noise signal to generate anti-phase noise so as to cancel external noise. The external residual noise signal and the internal noise signal are effectively identified, the filtering parameters of the adaptive filter are accurately estimated, and the noise elimination performance is improved.
In one embodiment, in step S110, the step of detecting the error noise signal includes at least one of:
Calculating an intensity value T1 corresponding to an error noise signal e1 (T) at the current time T, and increasing the error noise signal when the intensity value is larger than a first preset value;
calculating the difference value between the error noise signal e1 (t) at the current time t and the error noise signal e1 (t-1) at the previous time t-1, and increasing the error noise signal when the difference value is larger than a second preset value;
and calculating the difference value between the intensity value T3 corresponding to the error noise signal e1 (T2-T1) in the current time period T2-T1 and the intensity value T4 corresponding to the error noise signal e1 (T1-T0) in the previous time period T1-T0, and increasing the error noise signal when the difference value is larger than a third preset value.
In one example, the error noise change of two adjacent time points can be tracked, the error noise change of two adjacent time periods can be tracked, and whether the error noise signal is increased more obviously can be detected by comparing the current error noise signal with the previous two noise signals and other modes. The first preset value, the second preset value and the third preset value are all experience values obtained in debugging, and are related to actual hardware, and the specific values are adaptively adjusted according to actual conditions.
In one embodiment, the method further comprises:
Step S101: performing feature analysis on the reference noise signal to obtain a reference feature parameter S ref Reference to characteristic parameter S ref Comprising N reference eigenvalues and N sets of vectors U i And V i (i=1, 2, … N), N being the number of reference microphones for acquiring the reference noise signal.
In one example, a reference noise signal is input to a signal feature analyzer, and the reference noise signal is subjected to feature analysis to obtain a reference feature parameter S ref Comprising a sequence of characteristic values D ref Characteristic value sequence D ref Including N reference feature values.
In one embodiment, in step S110, the step of detecting the reference noise signal includes:
step S111: comparing N reference characteristic values corresponding to the t-1 moment and N reference characteristic values corresponding to the t moment with a fourth preset value successively to obtain the number N of characteristic values larger than the fourth preset value ref (t-1) and N ref (t)；
Step S112: at N ref (t) is less than or equal to N ref In the case of (t-1), the reference noise signal is not increased.
In one example, the reference noise change at two adjacent time points can be tracked, the reference noise change at two adjacent time periods can be tracked, and whether the reference noise signal is significantly increased can be detected by comparing the current reference noise signal with the previous two reference signals or the like. The fourth preset value is an empirical value obtained in debugging, and is related to actual hardware, and the specific value is adaptively adjusted according to actual conditions.
In one embodiment, the method further comprises:
step S102: performing feature analysis on the error noise signal to obtain an error feature parameter S err Error characteristic parameter S err Comprising M error characteristic values and M groups of vectors U j And V j (j=1, 2, … M), M being the acquisition error noiseThe number of error microphones of the signal and M error characteristic values form a set D err 。
In one example, an error noise signal is input to a signal feature analyzer, and feature analysis is performed on the error noise signal to obtain an error feature parameter S err Comprising a sequence of characteristic values D err Characteristic value sequence D err Including M error feature values.
In one embodiment, in step S110, the step of determining that an internal noise signal exists in the error noise signal includes:
step S113: sequentially comparing the M error characteristic values with a fifth preset value to obtain the number M of error characteristic values larger than the fifth preset value inner ，M inner Error feature value and corresponding vector U inner And V inner Characteristic parameters S constituting an internal noise signal inner ；
Step S114: M-M inner Error feature value and corresponding vector U outer And V outer Characteristic parameter S constituting an external residual noise signal outer ；
Wherein M is inner The error characteristic values form a set D inner ，M-M inner The error characteristic values form a set D outer 。
In one example, because the external noise is mostly normally filtered, the external residual noise in the error noise signal is small. The internal noise is not filtered out, so the internal residual noise in the error noise signal is the original internal noise, and the error noise signal is a main part and is far greater than the external residual noise. The fifth preset value is a threshold set empirically to distinguish between residual noise after the original noise is normally filtered and the original noise itself. M is M inner The error characteristic values form a set D inner Since the portion larger than the fifth preset value is the original noise component, it is determined that the internal noise needs to be filtered out, and the other portion is determined as the residual noise after the external noise is normally filtered out. The fifth preset value is an empirical value obtained in debugging, and is related to actual hardware, and the specific value is suitable according to actual conditionsAnd (5) adjusting the adaptability.
In one embodiment, step S120 includes:
the internal noise signal e is calculated using the following formula inner And an external residual noise signal e outer
In one embodiment, the method further comprises:
step S160: in the case where no increase in the error noise signal is detected or it is determined that no internal noise signal is present in the error noise signal, the error noise signal is taken as an external residual noise signal.
In an example, in the case where no increase in the error noise signal is detected or it is determined that there is no internal noise signal in the error noise signal, the noise picked up by the error microphone is considered to include only the external noise that is not canceled, i.e., the residual noise, and the resulting error noise signal is the external residual noise signal in the present embodiment.
In another embodiment, as shown in fig. 5, there is provided an adaptive noise reduction device, including:
An internal noise signal confirmation module 110, configured to acquire a reference noise signal and an error noise signal, and determine that the internal noise signal exists in the error noise signal when an increase in the error noise signal is detected and the reference noise signal is not increased;
an error noise signal separation module 120, configured to perform signal component separation on the error noise signal to obtain an internal noise signal and an external residual noise signal;
a first filtering module 130, configured to update an internal noise filtering parameter with the internal noise signal, and filter the internal noise signal with the updated internal noise filtering parameter;
a second filtering module 140, configured to update an external noise filtering parameter using the external residual noise signal, and filter the reference noise signal using the updated external noise filtering parameter;
the inverse noise generation module 150 is configured to superimpose the filtered reference noise signal and the filtered internal noise signal, and generate inverse noise to cancel external noise.
In one embodiment, the internal noise signal confirmation module includes at least one of:
the first error noise detection submodule is used for calculating an intensity value T1 corresponding to an error noise signal e1 (T) at the current moment T, and the error noise signal is increased when the intensity value is larger than a first preset value;
The second error noise detection sub-module is used for calculating the difference value between the error noise signal e1 (t) at the current moment t and the error noise signal e1 (t-1) at the previous moment t-1, and the error noise signal is increased under the condition that the difference value is larger than a second preset value;
the third error noise detection sub-module is configured to calculate a difference between an intensity value T3 corresponding to the error noise signal e1 (T2-T1) in the current time period T2-T1 and an intensity value T4 corresponding to the error noise signal e1 (T1-T0) in the previous time period T1-T0, where the difference is greater than a third preset value.
In one embodiment, the method further comprises:
the reference noise characteristic analysis module is used for carrying out characteristic analysis on the reference noise signal to obtain a reference characteristic parameter S ref Reference to characteristic parameter S ref Comprising N reference eigenvalues and N sets of vectors U i And V i (i=1, 2, … N), N being the number of reference microphones for acquiring the reference noise signal.
In one embodiment, an internal noise signal confirmation module includes:
the reference noise detection sub-module is used for successively comparing the N reference characteristic values corresponding to the t-1 moment and the N reference characteristic values corresponding to the t moment with a fifth preset value to obtain the number N of characteristic values larger than the fifth preset value ref (t-1) and N ref (t); at N ref (t) is less than or equal to N ref In the case of (t-1), the reference noise signal is not increased.
In one embodiment, the method further comprises:
the error noise characteristic analysis module is used for carrying out characteristic analysis on the error noise signal to obtain an error characteristic parameter S err Error characteristic parameter S err Comprising M error characteristic values and M groups of vectors U j And V j (j=1, 2, … M), M being the number of error microphones for obtaining the error noise signal, M error eigenvalues forming set D err 。
In one embodiment, an internal noise signal confirmation module includes:
an internal noise characteristic parameter calculation sub-module for successively comparing M error characteristic values with a sixth preset value to obtain the number M of error characteristic values larger than the sixth preset value inner ，M inner Error feature value and corresponding vector U inner And V inner Characteristic parameters S constituting an internal noise signal inner ；
Residual noise characteristic parameter calculation sub-module for M-M inner Error feature value and corresponding vector U outer And V outer Characteristic parameter S constituting an external residual noise signal outer The method comprises the steps of carrying out a first treatment on the surface of the Wherein M is inner The error characteristic values form a set D inner ，M-M inner The error characteristic values form a set D outer 。
In one embodiment, an error noise signal separation module includes:
an error noise separation calculation sub-module for calculating an internal noise signal e using the following formula inner And an external residual noise signal e outer
In one embodiment, the method further comprises:
in the case where an increase in the error noise signal is detected or it is determined that there is no internal noise signal in the error noise signal, the error noise signal is taken as an external residual noise signal.
The functions of each module in each device of the embodiments of the present application may be referred to the corresponding descriptions in the above methods, and are not described herein again.
According to an embodiment of the present application, the present application also provides an electronic device and a readable storage medium.
As shown in fig. 5, a block diagram of an electronic device of an adaptive noise reduction method according to an embodiment of the present application is shown. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the applications described and/or claimed herein.
As shown in fig. 5, the electronic device includes: one or more processors 501, memory 502, and interfaces for connecting components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the electronic device, including instructions stored in or on memory to display graphical information of a graphical user interface (Graphical User Interface, GUI) on an external input/output device, such as a display device coupled to the interface. In other embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple electronic devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 501 is illustrated in fig. 5.
Memory 502 is a non-transitory computer readable storage medium provided by the present application. The memory stores instructions executable by the at least one processor to cause the at least one processor to perform an adaptive noise reduction method provided by the present application. The non-transitory computer readable storage medium of the present application stores computer instructions for causing a computer to perform an adaptive noise reduction method provided by the present application.
The memory 502 is used as a non-transitory computer readable storage medium for storing non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to an adaptive noise reduction method according to an embodiment of the present application. The processor 501 executes various functional applications of the server and data processing by running non-transitory software programs, instructions, and modules stored in the memory 502, i.e., implements an adaptive noise reduction method in the above-described method embodiments.
Memory 502 may include a storage program area that may store an operating system, at least one application program required for functionality, and a storage data area; the storage data area may store data created according to the use of an electronic device of an adaptive noise reduction method, etc. In addition, memory 502 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory 502 may optionally include memory located remotely from processor 501, which may be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device may further include: an input device 503 and an output device 504. The processor 501, memory 502, input devices 503 and output devices 504 may be connected by a bus or otherwise, for example in fig. 5.
The input device 503 may receive input numeric or character information and generate key signal inputs related to user settings and function controls of the electronic device described above, such as a touch screen, keypad, mouse, trackpad, touchpad, pointer stick, one or more mouse buttons, trackball, joystick, and like input devices. The output devices 504 may include a display device, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a light emitting diode (Light Emitting Diode, LED) display, and a plasma display. In some implementations, the display device may be a touch screen.
Various implementations of the systems and techniques described here can be implemented in digital electronic circuitry, integrated circuitry, application specific integrated circuits (Application Specific Integrated Circuits, ASIC), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
These computing programs (also referred to as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, programmable logic devices (programmable logic device, PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., CRT (Cathode Ray Tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local area network (Local Area Network, LAN), wide area network (Wide Area Network, WAN) and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present application may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution disclosed in the present application can be achieved, and are not limited herein.
The above embodiments do not limit the scope of the present application. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the scope of the present application.
Claims (18)
1. An adaptive noise reduction method, comprising:
acquiring a reference noise signal and an error noise signal, and determining that an internal noise signal exists in the error noise signal when the error noise signal is detected to be increased and the reference noise signal is not increased;
performing signal component separation on the error noise signal to obtain the internal noise signal and an external residual noise signal;
Updating an internal noise filtering parameter by using the internal noise signal, and filtering the internal noise signal by using the updated internal noise filtering parameter;
updating an external noise filtering parameter by using the external residual noise signal, and filtering the reference noise signal by using the updated external noise filtering parameter;
and superposing the filtered reference noise signal and the filtered internal noise signal to generate inverted noise so as to offset external noise.
2. The method of claim 1, wherein the step of detecting the error noise signal comprises at least one of:
calculating an intensity value T1 corresponding to an error noise signal e1 (T) at the current time T, wherein the error noise signal is increased under the condition that the intensity value is larger than a first preset value;
calculating a difference value between an error noise signal e1 (t) at the current time t and an error noise signal e1 (t-1) at the previous time t-1, wherein the error noise signal is increased under the condition that the difference value is larger than a second preset value;
and calculating a difference value between an intensity value T3 corresponding to the error noise signal e1 (T2-T1) in the current time period T2-T1 and an intensity value T4 corresponding to the error noise signal e1 (T1-T0) in the previous time period T1-T0, wherein the error noise signal is increased when the difference value is larger than a third preset value.
3. The method as recited in claim 1, further comprising:
performing feature analysis on the reference noise signal to obtain a reference feature parameter S ref The reference characteristic parameter S ref Comprising N reference eigenvalues and N sets of vectors U i And V i (i=1, 2, … N), N being the number of reference microphones for obtaining the reference noise signal.
4. A method according to claim 3, wherein the step of detecting the reference noise signal comprises:
comparing N reference characteristic values corresponding to the t-1 moment and N reference characteristic values corresponding to the t moment with a fifth preset value successively to obtain the number N of characteristic values larger than the fifth preset value ref (t-1) and N ref (t)；
At N ref (t) is less than or equal to N ref In the case of (t-1), the reference noise signal is not increased.
5. The method as recited in claim 4, further comprising:
performing feature analysis on the error noise signal to obtain an error feature parameter S err The saidError characteristic parameter S err Comprising M error characteristic values and M groups of vectors U j And V j (j=1, 2, … M), M being the number of error microphones for obtaining the error noise signal, the M error eigenvalues forming a set D err 。
6. The method of claim 5, wherein the step of determining that an internal noise signal is present in the error noise signal comprises:
Comparing the M error characteristic values with a sixth preset value successively to obtain the number M of error characteristic values larger than the sixth preset value inner ，M inner Error feature value and corresponding vector U inner And V inner Characteristic parameters S constituting the internal noise signal inner ；
M-M inner Error feature value and corresponding vector U outer And V outer Characteristic parameter S constituting the external residual noise signal outer ；
Wherein M is inner The error characteristic values form a set D inner ，M-M inner The error characteristic values form a set D outer 。
7. The method of claim 6, wherein separating the error noise signal into signal components to obtain the internal noise signal and the external residual noise signal comprises:
the internal noise signal e is calculated using the following formula inner And an external residual noise signal e outer
8. The method as recited in claim 1, further comprising:
in the case that the error noise signal is detected not to be increased or it is determined that there is no internal noise signal in the error noise signal, the error noise signal is taken as the external residual noise signal.
9. An adaptive noise reduction device, comprising:
an internal noise signal confirmation module, configured to acquire a reference noise signal and an error noise signal, and determine that an internal noise signal exists in the error noise signal when the error noise signal is detected to be increased and the reference noise signal is not increased;
The error noise signal separation module is used for separating signal components of the error noise signal to obtain the internal noise signal and the external residual noise signal;
the first filtering module is used for updating the internal noise filtering parameters by using the internal noise signals and filtering the internal noise signals by using the updated internal noise filtering parameters;
the second filtering module is used for updating external noise filtering parameters by using the external residual noise signals and filtering the reference noise signals by using the updated external noise filtering parameters;
and the inverse noise generation module is used for superposing the filtered reference noise signal and the filtered internal noise signal to generate inverse noise so as to offset external noise.
10. The apparatus of claim 9, wherein the internal noise signal confirmation module comprises at least one of:
the first error noise detection submodule is used for calculating an intensity value T1 corresponding to an error noise signal e1 (T) at the current moment T, and the error noise signal is increased under the condition that the intensity value is larger than a first preset value;
the second error noise detection sub-module is used for calculating the difference value between the error noise signal e1 (t) at the current moment t and the error noise signal e1 (t-1) at the previous moment t-1, and the error noise signal is increased under the condition that the difference value is larger than a second preset value;
The third error noise detection sub-module is configured to calculate a difference between an intensity value T3 corresponding to the error noise signal e1 (T2-T1) in the current time period T2-T1 and an intensity value T4 corresponding to the error noise signal e1 (T1-T0) in the previous time period T1-T0, where the difference is greater than a third preset value, and the error noise signal is increased.
11. The apparatus as recited in claim 9, further comprising:
the reference noise characteristic analysis module is used for carrying out characteristic analysis on the reference noise signal to obtain a reference characteristic parameter S ref The reference characteristic parameter S ref Comprising N reference eigenvalues and N sets of vectors U i And V i (i=1, 2, … N), N being the number of reference microphones for obtaining the reference noise signal.
12. The apparatus of claim 11, wherein the internal noise signal confirmation module comprises:
the reference noise detection sub-module is used for successively comparing N reference characteristic values corresponding to the t-1 moment and N reference characteristic values corresponding to the t moment with a fifth preset value to obtain the number N of characteristic values larger than the fifth preset value ref (t-1) and N ref (t); at N ref (t) is less than or equal to N ref In the case of (t-1), the reference noise signal is not increased.
13. The apparatus as recited in claim 12, further comprising:
the error noise characteristic analysis module is used for carrying out characteristic analysis on the error noise signal to obtain an error characteristic parameter S err The error characteristic parameter S err Comprising M error characteristic values and M groups of vectors U j And V j (j=1, 2, … M), M being the acquisition of the error noiseThe number of error microphones of the signal, the M error characteristic values form a set D err 。
14. The apparatus of claim 13, wherein the internal noise signal confirmation module comprises:
an internal noise characteristic parameter calculation sub-module for successively comparing the M error characteristic values with a sixth preset value to obtain the number M of error characteristic values greater than the sixth preset value inner ，M inner Error feature value and corresponding vector U inner And V inner Characteristic parameters S constituting the internal noise signal inner ；
Residual noise characteristic parameter calculation sub-module for M-M inner Error feature value and corresponding vector U outer And V outer Characteristic parameter S constituting the external residual noise signal outer The method comprises the steps of carrying out a first treatment on the surface of the Wherein M is inner The error characteristic values form a set D inner ，M-M inner The error characteristic values form a set D outer 。
15. The apparatus of claim 14, wherein the error noise signal separation module comprises:
An error noise separation calculation sub-module for calculating an internal noise signal e using the following formula inner And an external residual noise signal e outer
16. The apparatus as recited in claim 9, further comprising:
in the case that the error noise signal is detected not to be increased or it is determined that there is no internal noise signal in the error noise signal, the error noise signal is taken as the external residual noise signal.
17. An electronic device, comprising:
at least one processor; and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-9.
18. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-9.
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