CN117995157A - Hybrid multichannel self-adaptive active noise reduction method and related device thereof - Google Patents

Hybrid multichannel self-adaptive active noise reduction method and related device thereof Download PDF

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Publication number
CN117995157A
CN117995157A CN202410135209.2A CN202410135209A CN117995157A CN 117995157 A CN117995157 A CN 117995157A CN 202410135209 A CN202410135209 A CN 202410135209A CN 117995157 A CN117995157 A CN 117995157A
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China
Prior art keywords
data
noise reduction
filter
audio
feedback
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CN202410135209.2A
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Chinese (zh)
Inventor
邓刚
赵宏亮
欧阳梓俊
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Shenzhen Changfeng Imaging Equipment Co ltd
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Shenzhen Changfeng Imaging Equipment Co ltd
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Priority to CN202410135209.2A priority Critical patent/CN117995157A/en
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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
    • G10K11/178—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
    • G10K11/1785—Methods, e.g. algorithms; Devices
    • G10K11/17853—Methods, e.g. algorithms; Devices of the filter
    • G10K11/17854—Methods, e.g. algorithms; Devices of the filter the filter being an adaptive filter
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
    • G10K11/178—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
    • G10K11/1781—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
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R1/00—Details of transducers, loudspeakers or microphones
    • H04R1/10—Earpieces; Attachments therefor ; Earphones; Monophonic headphones
    • H04R1/1083—Reduction of ambient noise
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K2210/00—Details of active noise control [ANC] covered by G10K11/178 but not provided for in any of its subgroups
    • G10K2210/10—Applications
    • G10K2210/108—Communication systems, e.g. where useful sound is kept and noise is cancelled
    • G10K2210/1081—Earphones, e.g. for telephones, ear protectors or headsets
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R2460/00—Details of hearing devices, i.e. of ear- or headphones covered by H04R1/10 or H04R5/033 but not provided for in any of their subgroups, or of hearing aids covered by H04R25/00 but not provided for in any of its subgroups
    • H04R2460/01—Hearing devices using active noise cancellation

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  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Signal Processing (AREA)
  • Soundproofing, Sound Blocking, And Sound Damping (AREA)

Abstract

The invention relates to the field of audio processing, and discloses a hybrid multichannel self-adaptive active noise reduction method and a related device. The method comprises the following steps: receiving external noise, correspondingly generating N pieces of acquisition noise data, and carrying out filtering processing on the N pieces of acquisition noise data according to a preset acquisition filter set to correspondingly generate N pieces of acquisition filtering data; the feedforward noise reduction system adds and processes the N pieces of acquired filtering data and preset audio signal data to generate combined audio data; receiving mixed sound of external noise and corresponding playing sound of the combined audio data to obtain error audio data; modifying the preset feedback filter based on the residual data and the secondary residual data; and the N microphone systems carry out parameter adjustment processing on the acquisition filter set based on residual data to generate a new parameter acquisition filter set. According to the invention, the problem of noise interference in the audio playing process is solved, and effective noise reduction playing is realized.

Description

Hybrid multichannel self-adaptive active noise reduction method and related device thereof
Technical Field
The invention relates to the field of audio processing, in particular to a hybrid multichannel self-adaptive active noise reduction method and a related device.
Background
Along with the design that some smart phones cancelled the earphone hole, more and more people select bluetooth headset as listening music and instrument of conversation, in order to pursue better tone quality, bluetooth headset walks into the range of making an uproar earphone of making an uproar gradually.
In order to filter noise in the use process of the Bluetooth headset, the headset mainly carries out noise reduction through two methods, namely hybrid multichannel self-adaptive active noise reduction and passive noise reduction, noise is blocked by a sound absorbing material, a noise reduction circuit is generally arranged in the headset for receiving and analyzing external noise and generating sound with opposite phase to the external noise, and the noise is counteracted or weakened to achieve the purpose of noise reduction.
The exterior of most of the existing hybrid multi-channel adaptive active noise reduction earphone is provided with a single microphone, a double microphone and a plurality of microphones, and the existing hybrid multi-channel adaptive active noise reduction earphone is only used for noise reduction, and can not well solve the problem of noise interference in the audio playing process. Therefore, aiming at the technical problem of noise interference in the audio playing process of the current hybrid multichannel adaptive active noise reduction earphone, a new technology is needed to solve the current problem.
Disclosure of Invention
The invention mainly aims to solve the technical problem of noise interference in the audio playing process.
The first aspect of the present invention provides a hybrid multi-channel adaptive active noise reduction method, which is applied to a hybrid multi-channel adaptive active noise reduction system, and the hybrid multi-channel adaptive active noise reduction system includes: n microphone systems, feedforward noise reduction system, feedback noise reduction system, wherein, N is positive integer, hybrid multichannel self-adaptation initiative noise reduction method includes:
the method comprises the steps that N microphone systems receive external noise, correspondingly generate N pieces of collected noise data, filter the N pieces of collected noise data according to a preset collection filter set, correspondingly generate N pieces of collected filter data, and send the N pieces of collected filter data to the feedforward noise reduction system;
The feedforward noise reduction system receives N pieces of collected filter data, adds and processes the N pieces of collected filter data and preset audio signal data to generate combined audio data;
Playing the combined audio data, receiving mixed sound of external noise and corresponding playing sound of the combined audio data, obtaining error audio data, and generating residual data based on the audio signal data and the error audio data;
Transmitting the residual data to N microphone systems and the feedback noise reduction system;
The feedback noise reduction system receives the corresponding playing sound of the combined audio data, generates feedback audio data and receives the residual data;
generating secondary residual error data based on the feedback audio data and the residual error audio data, and modifying a preset feedback filter based on the residual error data and the secondary residual error data to generate a feedback filter with new parameters;
And the N microphone systems receive the residual data, and perform parameter adjustment processing on the acquisition filter set based on the residual data to generate a new parameter acquisition filter set.
Optionally, in a first implementation manner of the first aspect of the present invention, N microphone systems include: the method comprises the steps of referring to a microphone system, receiving external noise by N microphone systems, correspondingly generating N pieces of collected noise data, carrying out filtering processing on the N pieces of collected noise data according to a preset collection filter set, and correspondingly generating N pieces of collected filtering data, wherein the N pieces of collected filtering data comprise the following steps:
the reference microphone system receives external noise, generates reference noise data, and performs filtering processing on the reference noise data according to a preset reference filter to generate reference filtering data.
Optionally, in a second implementation manner of the first aspect of the present invention, the performing parameter adjustment processing on the collection filter set based on the residual data, and generating the collection filter set of new parameters includes:
and modifying the acquisition filter set by utilizing the residual data and the N acquisition filter data according to a preset NLMS algorithm to generate a new parameter acquisition filter set.
Optionally, in a third implementation manner of the first aspect of the present invention, the modifying the collection filter set according to a preset NLMS algorithm by using the residual data and the N collection filter data, and generating the collection filter set of new parameters includes:
Reading target acquisition filter data of N pieces of acquisition filter data, wherein the target acquisition filter data are provided with target acquisition filters corresponding to mapping in the acquisition filter set;
And according to a preset NLMS algorithm, carrying out parameter modification processing on the target acquisition filter by utilizing the target acquisition filtering data and the residual data to generate a new target acquisition filter.
Optionally, in a fourth implementation manner of the first aspect of the present invention, the modifying the preset feedback filter based on the residual data and the secondary residual data, and generating the feedback filter of the new parameter includes:
and modifying the preset feedback filter by utilizing the residual data and the secondary residual data according to a preset NLMS algorithm to generate a feedback filter with new parameters.
Optionally, in a fifth implementation manner of the first aspect of the present invention, the generating residual data based on the audio signal data and the error audio data includes:
playing audio signal data, and receiving attenuated sound of the played sound corresponding to the audio signal data through an acoustic path to obtain attenuated audio data;
And adding the attenuated audio data and the error audio data to generate residual data.
Optionally, in a sixth implementation manner of the first aspect of the present invention, after the generating secondary residual data based on the feedback audio data and the residual audio data, and modifying a preset feedback filter based on the residual data and the secondary residual data, generating a feedback filter of a new parameter, the method further includes:
Performing feedback filtering processing on the secondary residual data according to the feedback filter of the new parameter to generate feedback filtering data;
and sending the feedback filtering data to the feedforward noise reduction system.
The second aspect of the present invention provides a hybrid multichannel adaptive active noise reduction device, comprising: a memory and at least one processor, the memory having instructions stored therein, the memory and the at least one processor being interconnected by a line; the at least one processor invokes the instructions in the memory to cause the hybrid multi-channel adaptive active noise reduction device to perform the hybrid multi-channel adaptive active noise reduction method described above.
A third aspect of the present invention provides a computer readable storage medium having instructions stored therein that, when run on a computer, cause the computer to perform the hybrid multichannel adaptive active noise reduction method described above.
In the embodiment of the invention, the microphone array can be utilized to carry out mixed multichannel self-adaptive active noise reduction when not in communication, and the played audio can be added in the communication process to carry out cyclic mixed noise reduction processing by adopting the complementation of a feedforward system and a feedback system, so that better effects of voice enhancement and noise reduction are achieved, the tone quality of the audio playing is purer, the audio playing quality is improved, the use experience of a user is improved, the technical problem of noise interference in the current audio playing is solved, and the effective noise reduction playing is realized.
Drawings
FIG. 1 is a schematic diagram illustrating a hybrid multi-channel adaptive active noise reduction method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an embodiment of a hybrid multi-channel adaptive active noise reduction method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of one embodiment of steps 1071 of a hybrid multi-channel adaptive active noise reduction method according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of an embodiment of a hybrid multi-channel adaptive active noise reduction device according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a hybrid multichannel self-adaptive active noise reduction method and a related device.
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While the present disclosure has been illustrated in the drawings in some form, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and examples of the present disclosure are for illustrative purposes only and are not intended to limit the scope of the present disclosure.
In describing embodiments of the present disclosure, the term "comprising" and its like should be taken to be open-ended, i.e., including, but not limited to. The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first," "second," and the like, may refer to different or the same object. Other explicit and implicit definitions are also possible below.
For convenience of understanding, the following describes a specific flow of an embodiment of the present invention, please refer to fig. 1, fig. 1 is a schematic diagram illustrating a principle of a hybrid multi-channel adaptive active noise reduction method in an embodiment of the present invention, fig. 2 is a schematic diagram of an embodiment of a hybrid multi-channel adaptive active noise reduction method in an embodiment of the present invention, and the hybrid multi-channel adaptive active noise reduction method is applied to a hybrid multi-channel adaptive active noise reduction system, where the hybrid multi-channel adaptive active noise reduction system includes: n microphone systems, feedforward noise reduction system, feedback noise reduction system, wherein, N is positive integer, hybrid multichannel self-adaptation initiative noise reduction method includes:
101. The method comprises the steps that N microphone systems receive external noise, correspondingly generate N pieces of collected noise data, filter the N pieces of collected noise data according to a preset collection filter set, correspondingly generate N pieces of collected filter data, and send the N pieces of collected filter data to the feedforward noise reduction system;
In this embodiment, the whole system has 3 microphone systems, a feedforward noise reduction system and a feedback noise reduction system, and sounds of external noise X are received in the 3 microphone systems (No. 1, no. 2 and No. 3), and X1, X2 and X3 are generated through different acoustic paths respectively, and specifically, 3 different microphones can receive external noise. The collecting filters are W1, W2 and W3, the mutual relation of the collecting filter to the collecting filter data rx1, rx2 and rx3 is that rx1=w1×x1, rx2=w2×x2, and rx3=w3×x3 is time domain convolution, and then the collecting filter data rx1, rx2 and rx3 are sent to the feedforward noise reduction system.
Further, in step 101, the N microphone systems include: the method comprises the steps of referring to a microphone system, receiving external noise by N microphone systems, correspondingly generating N pieces of collected noise data, carrying out filtering processing on the N pieces of collected noise data according to a preset collection filter set, and correspondingly generating N pieces of collected filtering data, wherein the N pieces of collected filtering data comprise the following steps:
1011. The reference microphone system receives external noise, generates reference noise data, and performs filtering processing on the reference noise data according to a preset reference filter to generate reference filtering data.
In step 1011, the data of the external noise X is filtered by the microphone No.1 as the reference microphone, the microphone No. 2 as the external near-mouth microphone, and the microphone No. 3 as the external center microphone, to generate the reference filtered data W1.
102. The feedforward noise reduction system receives N pieces of collected filter data, adds and processes the N pieces of collected filter data and preset audio signal data to generate combined audio data;
In this embodiment, the feedforward noise reduction system receives 3 pieces of acquisition filter data rx1, rx2, rx3, and if audio signal data audio is turned on, adds the audio to the acquisition filter data rx1, rx2, rx3 to generate combined audio data. When no person is talking or music is played, the audio is zero, and the earphone only processes external noise. It should be noted that the "adding N pieces of the collected filter data and the preset audio signal data to generate the combined audio data" proposed in this step is not only adding N pieces of the collected filter data and the preset audio signal data to generate the combined audio data. But rather the collection of the acquisition filter data and the preset audio signal data contained on the open content can generate the combined audio data. The "feedback filtered data" in the subsequent embodiments can also be summed to obtain the combined audio data. The addition of step 102 is not a closed-form process but an inclusive process.
103. Playing the combined audio data, receiving mixed sound of external noise and corresponding playing sound of the combined audio data, obtaining error audio data, and generating residual data based on the audio signal data and the error audio data;
In the present embodiment, the audio data before playing is the acquisition filter data rx1, rx2, rx3 and audio signal data audio, note that feedback data has not been introduced here because the feedback data has not been generated yet. And generating feedback data after one cycle, wherein the combined audio data is a combination of rx1+rx2+rx3+audio+ rxwf, the external noise is attenuated by the earphone earmuffs, the remaining low-frequency noise and the corresponding playing sound are reverse-S (rx1+rx2+rx3+audio+ rxwf), and the playing is performed for counteracting the noise. The external noise X has an attenuation P coefficient through the microphone cover, and the external noise X is attenuated through the ear cover P, dn=x×p. The audio data is also played, and the data after acoustic mixing is dn-S (rx1+rx2+rx3+audio+ rxwf), that is, the audio is received by the error microphone No. 4. The above description is based on the fact that the feedback data is generated after one cycle, and the first cycle only needs to remove the derived feedback filtering data rxwf, so as to meet the change of the actual data.
When the physical and electrical deduction is carried out, when residual data are calculated, an audio source is also introduced when audio needs to be introduced and played, if audio is also audio playing of the audio data is strictly executed when audio is played, playing sound is attenuated through an acoustic path and is conducted to an error microphone to be overlapped into dn-S (rx1+rx2+rx3+audio+ rxwf) +audio.
When there is a residue, assuming that the residue is 95%, the residue data is domain, which may be expressed as follows:
remain＝(dn-S*(rx1+rx2+rx3+audio+rxwf))*0.95
At this time, residual data e= (dn-S (rx1+rx2+rx3+audio+ rxwf)) ×0.95+audio×s_hat.
When audio is played, only music or conversation is left as residual data after external noise is cancelled. If no audio is played, only the audio after the external noise is cancelled is used as residual data.
Further, in "generating residual data based on the audio signal data, the error audio data" the following steps may be performed:
1031. Playing audio signal data, and receiving attenuated sound of the played sound corresponding to the audio signal data through an acoustic path to obtain attenuated audio data;
1032. And adding the attenuated audio data and the error audio data to generate residual data.
In step 1031-1032, audio signal data audio is played first, attenuated audio data audio_s_hat of the audio signal data audio after attenuation is received, the attenuated audio data audio_s_hat is added to error audio data dn-S (rx1+rx2+rx3+audio+ rxwf) to generate residual data e=dn-S (rx1+rx2+rx3+audio+ rxwf) +audio_s_hat.
104. Transmitting the residual data to N microphone systems and the feedback noise reduction system;
In this embodiment, as shown in fig. 1, the residual data e is transmitted to the feedback noise reduction system and the N microphone systems.
105. The feedback noise reduction system receives the corresponding playing sound of the combined audio data, generates feedback audio data and receives the residual data;
106. Generating secondary residual error data based on the feedback audio data and the residual error audio data, and modifying a preset feedback filter based on the residual error data and the secondary residual error data to generate a feedback filter with new parameters;
In steps 105-106, the processing procedure of the feedback noise reduction system in receiving the parametric residual data e and the playback sound (rx1+rx2+rx3+audio+ rxwf) of the combined audio data is mainly described.
The residual data e and the play sound (rx1+rx2+rx3+audio+ rxwf) are combined to obtain secondary residual data e+ (rx1+rx2+rx3+audio+ rxwf) s_hat, the secondary residual data e+ (rx1+rx2+rx3+audio+ rxwf) s_hat and the original residual data are taken as input data bases, an LMS algorithm or an NLMS algorithm is utilized to carry out parameter modification on the feedback filter Wfb, and the modified step length is (e+ (rx1+rx2+rx3+audio+ rxwf) s_hat) e or a+ (rx1+rx2+rx3+audio+ rxwf) s_hat) e, and a is a constant. The modified Wfb is used as a filter to perform filtering processing on secondary residual data e+ (rx1+rx2+rx3+audio+ rxwf) s_hat, and feedback filtered data rxwf = (e+ (rx1+rx2+rx3+audio+ rxwf) s_hat) Wfb is generated and returned to the feedforward noise reduction system to perform a new cycle of noise reduction processing.
Further, in step 106, the following steps may be performed:
1061. and modifying the preset feedback filter by utilizing the residual data and the secondary residual data according to a preset NLMS algorithm to generate a feedback filter with new parameters.
In step 1061, two output parameters of the NLMS algorithm are residual data and the secondary residual data, and as a multiplied value, the value of Wfb of the feedback filter is adjusted as a basic step, so as to generate feedback filter Wfb data of a new parameter, and the value of feedback filter data rxwf = (e+ (rx1+rx2+rx3+audio+ rxwf) ×s_hat) Wfb can be modified based on the product of feedback filter Wfb, so as to finally affect noise reduction data of the whole system.
Further, after step 106, the following steps may be performed:
1062. performing feedback filtering processing on the secondary residual data according to the feedback filter of the new parameter to generate feedback filtering data;
1063. and sending the feedback filtering data to the feedforward noise reduction system.
In steps 1062-1063, the secondary residual data is convolved based on the values of the feedback filter of the new parameters to generate feedback filtered data rxwf = (e+ (rx1+rx2+rx3+audio+ rxwf) ×s_hat) Wfb, the data is sent back to the feedforward noise reduction system, the feedforward noise reduction system performs the calculation of the combined audio data as a combination of rx1+rx2+rx3+audio+ rxwf, and the corresponding playback sound after attenuation by the playback sound hood and the acoustic path is-S (rx1+rx2+rx3+audio+ rxwf), where the playback is performed by the reverse sound in order to cancel noise. The external noise X has an attenuation P coefficient through the microphone cover, at this time, the data before being transmitted to the error microphone is dn=x×p, and at this time, the audio of the audio data is also played, and the data after acoustic mixing is dn-S (rx1+rx2+rx3+audio+ rxwf), that is, the audio received by the error microphone No. 4.
107. And the N microphone systems receive the residual data, and perform parameter adjustment processing on the acquisition filter set based on the residual data to generate a new parameter acquisition filter set.
In this embodiment, reference may be made to the updating process of the collection filter set { W1, W2, W3} of the microphone system in fig. 1, the parameter adjustment algorithm may be a modification manner of LMS, and two parameters required by LMS are respectively adjusted by x1 after the external noise is weakened through the acoustic path and the residual data e, and after the data of the collection filter set { W1, W2, W3} is adjusted, the data of the residual data e is circularly affected, so that the noise reduction process is realized by cyclic iteration.
Further, in the "performing parameter adjustment processing on the collection filter set based on the residual data, to generate a collection filter set of new parameters" the following steps may be performed:
1071. And modifying the acquisition filter set by utilizing the residual data and the N acquisition filter data according to a preset NLMS algorithm to generate a new parameter acquisition filter set.
In step 1071, the parameter tuning algorithm of the microphone system is only an NLMS algorithm, and the residual data and the N pieces of collected filter data are used to modify the collected filter set to generate a new parameter collected filter set.
Specifically, referring to fig. 3, fig. 3 is a schematic diagram illustrating an embodiment of step 1071 of the hybrid multi-channel adaptive active noise reduction method according to an embodiment of the present invention, the following steps may be performed in step 1071:
10711. Reading target acquisition filter data of N pieces of acquisition filter data, wherein the target acquisition filter data are provided with target acquisition filters corresponding to mapping in the acquisition filter set; ;
10712. And according to a preset NLMS algorithm, carrying out parameter modification processing on the target acquisition filter by utilizing the target acquisition filtering data and the residual data to generate a new target acquisition filter.
In the steps 10711-10712, the actual mode of processing the collected filtered data may be direct circuit transmission, or may be a controller, to uniformly control the data filtering parameters, and at this time, in order to provide a data interface to the upper computer, the upper computer may manually modify the parameters of each filter. In the process of using the controller to control N data to be transmitted to N data, one target acquisition filter data needs to be locked from the acquisition filter data, for example, the data of x1 is locked, and then the target acquisition filter W1 with the mapping relation is found in the acquisition filter set. After locking the data to be one-to-one, using an NLMS algorithm, modifying step length by x1 x e x a, wherein a is common sense or a set parameter, and finally generating final W1 parameters through multiple loop iteration to generate a new target acquisition filter.
In the embodiment of the invention, the microphone array can be utilized to carry out mixed multichannel self-adaptive active noise reduction when not in communication, and the played audio can be added in the communication process to carry out cyclic mixed noise reduction processing by adopting the complementation of a feedforward system and a feedback system, so that better effects of voice enhancement and noise reduction are achieved, the tone quality of the audio playing is purer, the audio playing quality is improved, the use experience of a user is improved, the technical problem of noise interference in the current audio playing is solved, and the effective noise reduction playing is realized.
Fig. 4 is a schematic structural diagram of a hybrid multi-channel adaptive active noise reduction device according to an embodiment of the present invention, where the hybrid multi-channel adaptive active noise reduction device 400 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 410 (e.g., one or more processors) and a memory 420, one or more storage mediums 430 (e.g., one or more mass storage devices) storing applications 433 or data 432. Wherein memory 420 and storage medium 430 may be transitory or persistent storage. The program stored on the storage medium 430 may include one or more modules (not shown), each of which may include a series of instruction operations in the hybrid multi-channel adaptive active noise reduction device 400. Still further, the processor 410 may be configured to communicate with the storage medium 430 to execute a series of instruction operations in the storage medium 430 on the hybrid multi-channel adaptive active noise reduction device 400.
The hybrid-based multi-channel adaptive active noise reduction device 400 may also include one or more power supplies 440, one or more wired or wireless network interfaces 450, one or more input/output interfaces 460, and/or one or more operating systems 431, such as Windows Serve, mac OS X, unix, linux, free BSD, and the like. Those skilled in the art will appreciate that the hybrid multi-channel adaptive active noise reduction device structure shown in fig. 4 is not limiting and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
The present invention also provides a computer readable storage medium, which may be a non-volatile computer readable storage medium, and may also be a volatile computer readable storage medium, where instructions are stored in the computer readable storage medium, when the instructions are executed on a computer, cause the computer to perform the steps of the hybrid multichannel adaptive active noise reduction method.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Moreover, although operations are depicted in a particular order, this should be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims.

Claims (9)

1. The hybrid multi-channel adaptive active noise reduction method is characterized in that the hybrid multi-channel adaptive active noise reduction method is applied to a hybrid multi-channel adaptive active noise reduction system, and the hybrid multi-channel adaptive active noise reduction system comprises: n microphone systems, feedforward noise reduction system, feedback noise reduction system, wherein, N is positive integer, hybrid multichannel self-adaptation initiative noise reduction method includes:
the method comprises the steps that N microphone systems receive external noise, correspondingly generate N pieces of collected noise data, filter the N pieces of collected noise data according to a preset collection filter set, correspondingly generate N pieces of collected filter data, and send the N pieces of collected filter data to the feedforward noise reduction system;
The feedforward noise reduction system receives N pieces of collected filter data, adds and processes the N pieces of collected filter data and preset audio signal data to generate combined audio data;
Playing the combined audio data, receiving mixed sound of external noise and corresponding playing sound of the combined audio data, obtaining error audio data, and generating residual data based on the audio signal data and the error audio data;
Transmitting the residual data to N microphone systems and the feedback noise reduction system;
The feedback noise reduction system receives the corresponding playing sound of the combined audio data, generates feedback audio data and receives the residual data;
generating secondary residual error data based on the feedback audio data and the residual error audio data, and modifying a preset feedback filter based on the residual error data and the secondary residual error data to generate a feedback filter with new parameters;
And the N microphone systems receive the residual data, and perform parameter adjustment processing on the acquisition filter set based on the residual data to generate a new parameter acquisition filter set.
2. The hybrid multichannel adaptive active noise reduction method of claim 1, wherein the N microphone systems comprise: the method comprises the steps of referring to a microphone system, receiving external noise by N microphone systems, correspondingly generating N pieces of collected noise data, carrying out filtering processing on the N pieces of collected noise data according to a preset collection filter set, and correspondingly generating N pieces of collected filtering data, wherein the N pieces of collected filtering data comprise the following steps:
the reference microphone system receives external noise, generates reference noise data, and performs filtering processing on the reference noise data according to a preset reference filter to generate reference filtering data.
3. The hybrid multi-channel adaptive active noise reduction method of claim 1, wherein the performing parameter adjustment processing on the collection filter set based on the residual data, generating a collection filter set of new parameters comprises:
and modifying the acquisition filter set by utilizing the residual data and the N acquisition filter data according to a preset NLMS algorithm to generate a new parameter acquisition filter set.
4. The hybrid multi-channel adaptive active noise reduction method according to claim 3, wherein the modifying the collection filter set according to a preset NLMS algorithm by using the residual data and N collection filter data, and generating a collection filter set of new parameters includes:
Reading target acquisition filter data of N pieces of acquisition filter data, wherein the target acquisition filter data are provided with target acquisition filters corresponding to mapping in the acquisition filter set;
And according to a preset NLMS algorithm, carrying out parameter modification processing on the target acquisition filter by utilizing the target acquisition filtering data and the residual data to generate a target acquisition filter with new parameters.
5. The hybrid multi-channel adaptive active noise reduction method according to claim 1, wherein the modifying the preset feedback filter based on the residual data and the secondary residual data to generate a feedback filter with new parameters comprises:
and modifying the preset feedback filter by utilizing the residual data and the secondary residual data according to a preset NLMS algorithm to generate a feedback filter with new parameters.
6. The hybrid multichannel adaptive active noise reduction method of claim 1, wherein the generating residual data based on the audio signal data, the error audio data comprises:
playing audio signal data, and receiving attenuated sound of the played sound corresponding to the audio signal data through an acoustic path to obtain attenuated audio data;
And adding the attenuated audio data and the error audio data to generate residual data.
7. The hybrid multi-channel adaptive active noise reduction method according to claim 1, wherein after the generating secondary residual data based on the feedback audio data and the residual audio data, and modifying a preset feedback filter based on the residual data and the secondary residual data, generating a feedback filter with new parameters, the method further comprises:
Performing feedback filtering processing on the secondary residual data according to the feedback filter of the new parameter to generate feedback filtering data;
and sending the feedback filtering data to the feedforward noise reduction system.
8. A hybrid multichannel adaptive active noise reduction device, the hybrid multichannel adaptive active noise reduction device comprising: a memory and at least one processor, the memory having instructions stored therein, the memory and the at least one processor being interconnected by a line;
The at least one processor invoking the instructions in the memory to cause the hybrid multi-channel adaptive active noise reduction device to perform the hybrid multi-channel adaptive active noise reduction method of any of claims 1-7.
9. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the hybrid multichannel adaptive active noise reduction method of any of claims 1-7.
CN202410135209.2A 2024-01-31 2024-01-31 Hybrid multichannel self-adaptive active noise reduction method and related device thereof Pending CN117995157A (en)

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