CN114615586A - Earphone noise reduction method and device, electronic equipment and readable storage medium - Google Patents
Earphone noise reduction method and device, electronic equipment and readable storage medium Download PDFInfo
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- H04R1/00—Details of transducers, loudspeakers or microphones
- H04R1/10—Earpieces; Attachments therefor ; Earphones; Monophonic headphones
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Abstract
The application discloses a method and a device for reducing noise of an earphone, an electronic device and a readable storage medium, wherein the earphone is provided with a plurality of feedforward pickup paths, and the method for reducing noise of the earphone comprises the following steps: acquiring initial sound signals picked up by each feedforward picking-up path, and detecting whether unsteady state noise signals exist in each initial sound signal; if the unsteady state noise signals exist, detecting the unsteady state noise amplitude corresponding to each initial sound signal; selecting a feedforward pickup signal from each initial sound signal according to each unsteady noise amplitude; and performing earphone noise reduction according to the feedforward pickup signal. The application solves the technical problem of poor noise reduction effect of the earphone in the prior art.
Description
Technical Field
The present application relates to the field of sound processing technologies, and in particular, to a method and an apparatus for reducing noise of an earphone, an electronic device, and a readable storage medium.
Background
At present, when the noise of the earphone is reduced, the noise can be accurately eliminated through a feedforward microphone and a feedback microphone. The feedforward microphone collects outside environment noise and counteracts reversely, the feedback microphone detects residual noise in an ear canal and performs feedback inhibition, and the specific principle is as follows: based on the amplitude or phase information of the noise signal picked up by the MIC, a corresponding signal filter process is performed. However, when the noise signal is an unsteady noise signal, the feedforward microphone usually cannot accurately capture the signal characteristics of the unsteady noise signal, and thus the noise reduction effect of the earphone is affected.
Disclosure of Invention
The present application mainly aims to provide a method and an apparatus for reducing noise of a headphone, an electronic device, and a readable storage medium, and aims to solve the technical problem in the prior art that the noise reduction effect of a headphone is poor.
In order to achieve the above object, the present application provides a method for reducing noise of a headphone, where the headphone is provided with a plurality of feed-forward pickup paths, the method comprising:
acquiring initial sound signals picked up by each feedforward picking-up path, and detecting whether unsteady state noise signals exist in each initial sound signal;
if the unsteady state noise signals exist, detecting the unsteady state noise amplitude corresponding to each initial sound signal;
selecting a feedforward pickup signal from each initial sound signal according to each unsteady noise amplitude;
and performing earphone noise reduction according to the feedforward pickup signal.
Optionally, the step of selecting a feedforward pickup signal in each of the initial sound signals according to each of the unsteady noise amplitudes comprises:
if the amplitude of each unsteady state noise is larger than a preset noise amplitude threshold value, determining a first target amplitude with the minimum amplitude in each unsteady state noise amplitude;
taking the initial sound signal corresponding to the first target amplitude as the feedforward pickup signal;
if the unsteady state noise amplitudes are not all larger than the preset noise amplitude threshold, determining second target amplitudes smaller than the preset noise amplitude threshold in the unsteady state noise amplitudes;
and selecting the feedforward pickup signal from the initial sound signals corresponding to the second target amplitudes.
Optionally, the step of detecting an unsteady noise amplitude corresponding to each of the initial sound signals includes:
respectively extracting audio features corresponding to the initial sound signals;
and respectively inputting each audio characteristic into a preset signal amplitude detection model, and detecting the unsteady state noise amplitude corresponding to each initial sound signal.
Optionally, the preset signal amplitude detection model comprises a first feature extractor,
the step of detecting the unsteady state noise amplitude corresponding to each initial sound signal by inputting each audio characteristic into a preset signal amplitude detection model respectively comprises:
extracting an unsteady noise amplitude feature in the audio features by inputting the audio features into the first feature extractor;
and determining the corresponding unsteady state noise amplitude according to the unsteady state noise amplitude characteristic.
Optionally, the step of detecting whether there is an unsteady noise signal in each of the initial sound signals includes:
respectively carrying out feature recognition on the audio features of the initial sound signals according to a preset noise feature recognition model to obtain feature recognition results corresponding to the initial sound signals;
and determining whether the unsteady noise signal exists in each initial sound signal according to each feature recognition result.
Optionally, the preset noise feature recognition model comprises a second feature extractor, the feature recognition result comprises a feature class label,
the step of respectively performing feature recognition on the audio features of the initial sound signals according to a preset noise feature recognition model to obtain feature recognition results corresponding to the initial sound signals comprises the following steps:
extracting non-stationary noise features in the audio features by inputting the audio features into the second feature extractor;
and classifying the unsteady noise characteristics to obtain the characteristic category labels.
Optionally, the step of performing headphone noise reduction according to the feed-forward pickup signal includes:
picking up the feedback signal through a feedback pick-up path of an earphone;
generating a corresponding feedforward anti-phase noise signal according to the feedforward pickup signal, and generating a corresponding feedback anti-phase noise signal according to the feedback signal;
and performing earphone noise reduction according to the feedforward reversed-phase noise signal and the feedback reversed-phase noise signal.
In order to achieve the above object, the present application further provides a noise reduction device for a headphone, the headphone being provided with a plurality of feedforward pickup paths, the noise reduction device for a headphone including:
the first detection module is used for acquiring initial sound signals picked up by different feed-forward pickup paths of the earphone and detecting whether unsteady-state noise signals exist in the initial sound signals or not;
the second detection module is used for detecting the unsteady state noise amplitude corresponding to each initial sound signal if the unsteady state noise signal exists;
a selecting module, configured to select a feedforward pickup signal from each of the initial sound signals according to each of the unsteady noise amplitudes;
and the noise reduction module is used for performing earphone noise reduction according to the feedforward pickup signal.
Optionally, the selecting module is further configured to:
if the amplitude of each unsteady state noise is larger than a preset noise amplitude threshold value, determining a first target amplitude with the minimum amplitude in each unsteady state noise amplitude;
taking the initial sound signal corresponding to the first target amplitude as the feedforward pickup signal;
if the non-steady-state noise amplitudes are not all larger than the preset noise amplitude threshold, determining second target amplitudes smaller than the preset noise amplitude threshold in the non-steady-state noise amplitudes;
and selecting the feedforward pickup signal from the initial sound signals corresponding to the second target amplitudes.
Optionally, the first detection module is further configured to:
respectively extracting audio features corresponding to the initial sound signals;
and respectively inputting each audio characteristic into a preset signal amplitude detection model to detect the unsteady state noise amplitude corresponding to each initial sound signal.
Optionally, the preset signal amplitude detection model includes a first feature extractor, and the first detection module is further configured to:
extracting an unsteady noise amplitude feature in the audio features by inputting the audio features into the first feature extractor;
and determining the corresponding unsteady state noise amplitude according to the unsteady state noise amplitude characteristic.
Optionally, the second detection module is further configured to:
respectively carrying out feature recognition on the audio features of the initial sound signals according to a preset noise feature recognition model to obtain feature recognition results corresponding to the initial sound signals;
and determining whether the unsteady noise signal exists in each initial sound signal according to each feature recognition result.
Optionally, the preset noise feature recognition model includes a second feature extractor, and the second detection module is further configured to:
extracting non-stationary noise features in the audio features by inputting the audio features into the second feature extractor;
and classifying the unsteady noise characteristics to obtain the characteristic category label.
Optionally, the noise reduction module is further configured to:
picking up the feedback signal through a feedback pick-up path of a headset;
generating a corresponding feedforward anti-phase noise signal according to the feedforward pickup signal, and generating a corresponding feedback anti-phase noise signal according to the feedback signal;
and performing earphone noise reduction according to the feedforward reversed-phase noise signal and the feedback reversed-phase noise signal.
The present application further provides an electronic device, the electronic device is an entity device, the electronic device includes: a memory, a processor and a program of the headphone noise reduction method stored on the memory and executable on the processor, which program, when executed by the processor, may implement the steps of the headphone noise reduction method as described above.
The present application also provides a computer-readable storage medium having stored thereon a program for implementing a method of noise reduction for headphones, which program, when executed by a processor, implements the steps of the method of noise reduction for headphones as described above.
The present application also provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of the above-described headphone noise reduction method.
The application provides a method and a device for reducing noise of an earphone, electronic equipment and a readable storage medium, namely acquiring initial sound signals picked up by different feedforward pickup paths of the earphone, and detecting whether unsteady-state noise signals exist in the initial sound signals; if the initial sound signals exist, selecting feedforward pickup signals from the initial sound signals according to unsteady noise amplitudes corresponding to the initial sound signals; and performing earphone noise reduction according to the feedforward pickup signal. A plurality of feedforward pickup paths are arranged for the earphone to pick up initial sound signals, so that when unsteady noise signals exist in the external environment, the initial sound signals with small noise amplitude can be selected from the initial sound signals to serve as the feedforward pickup signals according to the amplitude of the unsteady noise signals carried in the initial sound signals, and the corresponding noise reduction process of the earphone is carried out according to the signal characteristics of the feedforward pickup signals, so that the feedforward pickup signals can be picked up by the pickup paths with large unsteady noise, the feedforward pickup signals can be picked up by the pickup paths with small unsteady noise, the purpose of noise reduction of the earphone by the feedforward pickup signals with small unsteady noise amplitude is achieved, and the noise reduction effect of the earphone is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a schematic flowchart of a first embodiment of a method for noise reduction of a headphone according to the present application;
FIG. 2 is a schematic diagram illustrating noise reduction according to feedforward control and feedback control in the method for reducing noise in an earphone according to the present application;
fig. 3 is a schematic flowchart of a second embodiment of the noise reduction method for headphones according to the present application;
fig. 4 is a schematic device structure diagram of a hardware operating environment related to the noise reduction method for the headset in the embodiment of the present application.
The objectives, features, and advantages of the present application will be further described with reference to the accompanying drawings.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, embodiments of the present application are described in detail below with reference to the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Example one
The embodiment of the application provides a method for reducing noise of an earphone, which is applied to VR equipment, wherein in a first embodiment of the method for reducing noise of the earphone, the earphone is provided with a plurality of feedforward pickup paths, and the method for reducing noise of the earphone comprises the following steps:
step S10, acquiring initial sound signals picked up by each feedforward pickup path, and detecting whether there is an unsteady noise signal in each initial sound signal;
step S20, if there is unsteady state noise signal, detecting unsteady state noise amplitude corresponding to each initial sound signal;
step S30, selecting a feedforward pickup signal from each of the initial sound signals according to each of the unsteady noise amplitudes;
and step S40, performing earphone noise reduction according to the feedforward pickup signal.
In this embodiment, it should be noted that, at present, a feedforward pickup path and a feedback pickup path are usually disposed on the earphone, the feedforward pickup path may be a feedforward microphone, the feedback pickup path may be a feedback microphone, the feedforward pickup path is used to pick up outside ambient noise for performing reverse cancellation, and the feedback pickup path is used to pick up residual noise inside the ear canal for performing feedback suppression. However, unsteady noise often exists in an actual application scene, the unsteady noise is noise without steady-state characteristics, the steady-state characteristics may be amplitude characteristics or phase characteristics with a certain distribution rule, and the like. The feedforward pickup signal is a sound signal with unsteady noise which does not affect the auditory experience of the user in each initial sound signal, or a sound signal with unsteady noise which has the smallest influence on the auditory experience of the user in each initial sound signal, wherein the auditory experience of the user is related to the amplitude of the unsteady noise, when the amplitude of the unsteady noise is smaller than a preset noise amplitude threshold value, the auditory experience of the user is hardly affected, and when the amplitude of the unsteady noise is not smaller than the preset noise amplitude threshold value, the larger the amplitude of the unsteady noise is, the worse the auditory experience of the user is.
As one example, steps S10 to S20 include: picking up initial sound signals in an external environment through a plurality of feedforward microphones of an earphone, wherein the feedforward microphones are arranged at different preset positions of the earphone; respectively judging whether each initial sound signal is an unsteady state noise signal according to a preset noise judgment model, and detecting whether the unsteady state noise signal exists in each initial sound signal, wherein the unsteady state noise signal can be a sound signal carrying unsteady state noise; if unsteady noise signals exist in the initial sound signals, performing feature extraction on the initial sound signals to extract unsteady noise amplitude in the initial sound signals; selecting a sound signal with the minimum unsteady state noise amplitude from the initial sound signals as a feedforward pickup signal; and generating a feedforward reverse-phase noise signal corresponding to the feedforward pickup signal according to the current mode of the earphone, and carrying out earphone noise reduction according to the feedforward reverse-phase noise signal.
Additionally, it should be noted that, since unsteady noise is usually generated at a nearby position of the earphone, there will be a difference in amplitude of unsteady noise picked up at different positions of the earphone, for example, when the unsteady noise is noise that hair scratches the earphone, hair will usually scratch the area above the ear stem of the earphone, so that the amplitude of unsteady noise picked up by the microphone arranged in the area above the ear stem is large, and the amplitude of unsteady noise picked up by the microphone arranged in the area below the ear stem is small, so in the embodiment of the present application, a plurality of feedforward microphones are arranged at different preset positions of the earphone as feedforward pickup paths, so that when there is unsteady noise signal, by detecting the amplitude of unsteady noise of initial sound signal picked up by each feedforward microphone, a feedforward pickup path with the smallest amplitude of unsteady noise is selected from each feedforward microphone, therefore, the influence of unsteady noise on the noise reduction of the earphone is reduced as much as possible, and the noise reduction effect of the earphone can be improved.
Wherein the step of detecting the non-stationary noise amplitude corresponding to each of the initial sound signals comprises:
step S11, respectively extracting the audio features corresponding to the initial sound signals;
step S12, inputting each audio characteristic into a preset signal amplitude detection model, and detecting an unsteady state noise amplitude corresponding to each initial sound signal.
As one example, steps S11 to S12 include: converting the initial sound signal from a time domain to a frequency domain to obtain a frequency domain sound signal; performing feature extraction on the frequency domain sound signal to obtain audio features; the audio features are input into a preset signal amplitude detection model, the audio features are mapped into corresponding model output values, and the model output values are used as unsteady state noise amplitudes corresponding to the audio features. The preset signal amplitude detection model may be a logistic regression model, and the model output value may be a logistic regression value. The embodiment of the application realizes the purpose of detecting the amplitude of the non-steady-state noise in the sound signal by combining with deep learning, and can estimate the amplitude of the non-steady-state noise in the sound signal.
As an example, the performing feature extraction on the frequency domain sound signal to obtain an audio feature includes: the method includes the steps that in a preset frequency range and a preset frequency range, periodic sampling is conducted on frequency-domain sound signals for preset times, so that a preset number of frequency response values are collected in the frequency-domain sound signals, and audio features are obtained, for example, the preset frequency range and the preset frequency range can be set by a user, the preset frequency can be set to be 20-1000 Hz, the preset frequency range is set to be one twelfth of the frequency range, the sampling period is set to be 100ms, the preset number of the periodic sampling times is 32 times, and the preset number is 32, a vector formed by 32 frequency response values is obtained in each sampling, 32 vectors are totally formed, a matrix of 32 x 32 is formed by the 32 vectors, and the matrix of 32 x 32 is the audio features.
The step of detecting the unsteady noise amplitude corresponding to each initial sound signal by inputting each audio feature into the preset signal amplitude detection model respectively comprises:
step S121, extracting unsteady noise amplitude characteristics in the audio frequency characteristics by inputting the audio frequency characteristics into the first characteristic extractor;
and S122, determining the corresponding unsteady state noise amplitude according to the unsteady state noise amplitude characteristic.
As one example, steps S121 to S122 include: inputting the audio features into the first feature extractor for feature transformation, so as to map the audio features to a preset first feature dimension, and obtain unsteady-state noise amplitude features, wherein the preset first feature dimension may be a preset matrix size or a preset vector length, and the unsteady-state noise amplitude features may be a preset size feature matrix or a preset length feature vector; and carrying out full connection on the unsteady state noise amplitude characteristics to obtain a full connection vector, and mapping the full connection vector into the unsteady state noise amplitude. The non-steady state noise amplitude characteristic is a characteristic representing amplitude information of non-steady state noise in the audio characteristic, and the amplitude information can be amplitude, amplitude distribution condition and the like.
As an example, step S122 further includes: determining a target amplitude characteristic with the highest similarity to the unsteady state noise amplitude characteristic in each preset amplitude characteristic by calculating the similarity between the unsteady state noise amplitude characteristic and each preset amplitude characteristic; and determining the noise amplitude corresponding to the target amplitude characteristic as the unsteady state noise amplitude according to the corresponding relation between the preset amplitude characteristic and the amplitude.
Wherein the step of detecting whether an unsteady noise signal exists in each of the initial sound signals includes:
step S21, respectively carrying out feature recognition on the audio features of the initial sound signals according to a preset noise feature recognition model to obtain feature recognition results corresponding to the initial sound signals;
step S22 is to determine whether the unsteady noise signal exists in each of the initial sound signals according to each of the feature recognition results.
As one example, steps S21 to S22 include: acquiring audio features corresponding to the initial sound signals; inputting the audio features into the preset noise feature recognition model, performing secondary classification on the audio features to obtain feature secondary classification labels, and taking the feature secondary classification labels as feature recognition results; judging whether a preset binary classification label exists in the characteristic binary classification labels corresponding to the initial sound signals, and if so, judging that an unsteady state noise signal exists in the initial sound signals; if not, judging that the unsteady noise signal does not exist in each initial sound signal. For example, a feature two classification label may be set to be 1 or 0, where 1 identifies that the audio feature is a feature of an unsteady noise signal, and 0 identifies that the audio feature is not a feature of an unsteady noise signal, so as to set a preset two classification label to be 1, if there is a 1 in each feature two classification label, it is determined that an unsteady noise signal exists in each initial sound signal, and if there is no 1 in each feature two classification label, it is determined that an unsteady noise signal does not exist in each initial sound signal. The embodiment of the application realizes the purpose of detecting the unsteady state noise signal by combining with deep learning, and can accurately judge whether the sound signal is the unsteady state noise signal.
The preset noise feature recognition model comprises a second feature extractor, the feature recognition result comprises a feature category label, and the step of respectively performing feature recognition on the audio features of the initial sound signals according to the preset noise feature recognition model to obtain the feature recognition results corresponding to the initial sound signals comprises the following steps of:
step S211, extracting unsteady-state noise characteristics in the audio characteristics by inputting the audio characteristics into the second characteristic extractor;
step S212, the unsteady state noise features are classified to obtain the feature class labels.
In this embodiment, it should be noted that the unsteady noise characteristic is a characteristic that characterizes unsteady noise information in the audio characteristic, and the unsteady noise information may be one or more of phase information, amplitude information, and frequency information.
As an example, steps S211 to S212 include: inputting the audio features into a second feature extractor for feature transformation, so as to map the audio features to a preset second feature dimension, and obtain unsteady-state noise features, wherein the preset second feature dimension may be a preset matrix size or a length of a preset vector, and the unsteady-state noise features may be a feature matrix of a preset size or a feature vector of a preset length; and inputting the unsteady noise characteristics into a classifier in the preset noise characteristic identification model for classification to obtain the characteristic category label, wherein the characteristic category label can be a characteristic two-classification label.
Wherein the step of performing headphone noise reduction in accordance with the feed-forward pickup signal comprises:
step S41, picking up the feedback signal through a feedback pick-up path of the headphone;
step S42, generating a corresponding feedforward anti-phase noise signal according to the feedforward pickup signal, and generating a corresponding feedback anti-phase noise signal according to the feedback signal;
and step S43, performing earphone noise reduction according to the feedforward reversed phase noise signal and the feedback reversed phase noise signal.
In this embodiment, it should be noted that, in an active noise reduction mode or a transparent transmission mode of the earphone, a feedforward sound signal and a feedback sound signal may be picked up to reduce noise, and referring to fig. 2, fig. 2 is a schematic diagram illustrating a principle of noise reduction according to feedforward control and feedback control in the embodiment of the present application, where a feedforward MIC is a feedforward microphone, a feedback MIC is a feedback microphone, a feedforward PCB is feedforward control for generating a feedforward inverse noise electrical signal, and a feedback PCB is feedback control for generating a feedback inverse noise electrical signal.
As one example, steps S41 to S43 include: picking up the feedback signal through a feedback pick-up path of a headset; generating a corresponding feedforward reverse-phase noise signal according to the amplitude and phase information of the feedforward pickup signal; generating a corresponding feedback inverted noise signal according to the amplitude and phase information of the feedback signal; and combining the feedforward reversed phase noise signal and the feedback reversed phase noise signal into a reversed phase noise signal, and performing earphone noise reduction according to the reversed phase noise signal.
The embodiment of the application provides a noise reduction method for an earphone, namely, initial sound signals picked up by different feedforward pickup paths of the earphone are obtained, and whether unsteady state noise signals exist in the initial sound signals or not is detected; if the initial sound signals exist, selecting feedforward pickup signals from the initial sound signals according to unsteady noise amplitudes corresponding to the initial sound signals; and performing earphone noise reduction according to the feedforward pickup signal. In the embodiment of the application, a plurality of feedforward pickup paths are arranged for the earphone to pick up the initial sound signals, so that when unsteady noise signals exist in the external environment, the initial sound signals with smaller noise amplitude can be selected from the initial sound signals to serve as the feedforward pickup signals according to the amplitude of the unsteady noise signals carried in the initial sound signals, and the corresponding noise reduction process of the earphone is carried out according to the signal characteristics of the feedforward pickup signals, so that the feedforward pickup signals picked up by the pickup paths with larger unsteady noise amplitudes can be avoided, the feedforward pickup signals are picked up by the pickup paths with smaller unsteady noise amplitudes, the purpose of performing the noise reduction of the earphone by the feedforward pickup signals with smaller unsteady noise amplitudes is realized, and the noise reduction effect of the earphone is improved.
Example two
Further, referring to fig. 3, in another embodiment of the present application, the same or similar contents as those in the first embodiment may refer to the above description, and are not repeated herein. On this basis, the step of selecting a feedforward pickup signal in each of the initial sound signals according to each of the unsteady noise amplitudes includes:
step S31, if each of the non-steady state noise amplitudes is greater than a preset noise amplitude threshold, determining a first target amplitude with a minimum amplitude in each of the non-steady state noise amplitudes;
step S32, using the initial sound signal corresponding to the first target amplitude as the feedforward pickup signal;
step S33, if the unsteady state noise amplitudes are not all greater than the preset noise amplitude threshold, determining second target amplitudes smaller than the preset noise amplitude threshold in the unsteady state noise amplitudes;
in step S34, the feedforward pickup signal is selected from the initial sound signals corresponding to the second target amplitudes.
In this embodiment, it should be noted that the preset noise amplitude threshold is a threshold set according to the user's headphone hearing experience, when the amplitude of the noise is smaller than the preset noise amplitude threshold, the noise does not affect the user's headphone hearing experience, when the amplitude of the noise is not smaller than the preset noise amplitude threshold, the noise affects the user's headphone hearing experience, and the larger the amplitude of the noise is, the worse the user's headphone hearing experience is.
As one example, steps S31 to S35 include: comparing each non-steady-state noise amplitude with a preset noise amplitude threshold value respectively, and judging whether each non-steady-state noise amplitude is larger than the preset noise amplitude threshold value or not; if the amplitude of each unsteady state noise is larger than a preset noise amplitude threshold value, determining a first target amplitude with the minimum amplitude in the amplitude of each unsteady state noise, and taking an initial sound signal corresponding to the first target amplitude as the feedforward pickup signal; if the unsteady noise amplitudes are not all larger than the preset noise amplitude threshold, determining second target amplitudes smaller than the preset noise amplitude threshold in the unsteady noise amplitudes, wherein the unsteady noise in the initial sound signal corresponding to the second target amplitudes does not influence the hearing experience of the earphone of the user; and randomly selecting the feedforward pickup signal from the initial sound signals corresponding to the second target amplitudes, or selecting the sound signal with the minimum amplitude from the initial sound signals corresponding to the second target amplitudes as the feedforward pickup signal.
The embodiment of the application provides a feedforward signal selection method based on multiple pickup paths, namely if each unsteady state noise amplitude is larger than a preset noise amplitude threshold value, determining a first target amplitude with the minimum amplitude in each unsteady state noise amplitude; taking the initial sound signal corresponding to the first target amplitude as the feedforward pickup signal; if the unsteady state noise amplitudes are not all larger than the preset noise amplitude threshold, determining second target amplitudes smaller than the preset noise amplitude threshold in the unsteady state noise amplitudes; and selecting the feedforward pickup signal from the initial sound signals corresponding to the second target amplitudes. The method and the device achieve the purpose of picking the feedforward signals by selecting the feedforward pickup path with the minimum auditory experience of the user from the plurality of feedforward pickup paths, can avoid picking the feedforward pickup signals by the pickup path with unsteady noise with large amplitude, and lay a foundation for improving the noise reduction effect of the earphone.
EXAMPLE III
The present application further provides a device of making an uproar falls in earphone, the earphone is provided with a plurality of feedforward and picks up the route, the device of making an uproar falls in earphone includes:
the first detection module is used for acquiring initial sound signals picked up by different feedforward pickup paths of the earphone and detecting whether unsteady state noise signals exist in the initial sound signals or not;
the second detection module is used for detecting the unsteady state noise amplitude corresponding to each initial sound signal if the unsteady state noise signal exists;
a selecting module for selecting a feedforward pickup signal in each of the initial sound signals according to each of the non-stationary noise amplitudes;
and the noise reduction module is used for performing earphone noise reduction according to the feedforward pickup signal.
Optionally, the selecting module is further configured to:
if the amplitude of each unsteady state noise is larger than a preset noise amplitude threshold value, determining a first target amplitude with the minimum amplitude in each unsteady state noise amplitude;
taking the initial sound signal corresponding to the first target amplitude as the feedforward pickup signal;
if the unsteady state noise amplitudes are not all larger than the preset noise amplitude threshold, determining second target amplitudes smaller than the preset noise amplitude threshold in the unsteady state noise amplitudes;
and selecting the feedforward pickup signal from the initial sound signals corresponding to the second target amplitudes.
Optionally, the first detection module is further configured to:
respectively extracting audio features corresponding to the initial sound signals;
and respectively inputting each audio characteristic into a preset signal amplitude detection model, and detecting the unsteady state noise amplitude corresponding to each initial sound signal.
Optionally, the preset signal amplitude detection model includes a first feature extractor, and the first detection module is further configured to:
extracting non-stationary noise amplitude features in the audio features by inputting the audio features into the first feature extractor;
and determining the corresponding unsteady state noise amplitude according to the unsteady state noise amplitude characteristic.
Optionally, the second detection module is further configured to:
respectively carrying out feature recognition on the audio features of the initial sound signals according to a preset noise feature recognition model to obtain feature recognition results corresponding to the initial sound signals;
and determining whether the unsteady noise signal exists in each initial sound signal according to each feature recognition result.
Optionally, the preset noise feature recognition model includes a second feature extractor, and the second detection module is further configured to:
extracting non-stationary noise features in the audio features by inputting the audio features into the second feature extractor;
and classifying the unsteady noise characteristics to obtain the characteristic category label.
Optionally, the noise reduction module is further configured to:
picking up the feedback signal through a feedback pick-up path of a headset;
generating a corresponding feedforward anti-phase noise signal according to the feedforward pickup signal, and generating a corresponding feedback anti-phase noise signal according to the feedback signal;
and performing earphone noise reduction according to the feedforward reversed-phase noise signal and the feedback reversed-phase noise signal.
The earphone noise reduction device provided by the application adopts the earphone noise reduction method in the embodiment, and the technical problem of poor noise reduction effect of the earphone is solved. Compared with the prior art, the beneficial effects of the earphone noise reduction device provided by the embodiment of the application are the same as those of the earphone noise reduction method provided by the embodiment, and other technical features of the earphone noise reduction device are the same as those disclosed by the embodiment method, which are not repeated herein.
Example four
An embodiment of the present application provides an electronic device, the electronic device may be a VR device, and the electronic device includes: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method for reducing noise of a headset according to the first embodiment.
Referring now to FIG. 4, shown is a schematic diagram of an electronic device suitable for use in implementing embodiments of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 4, the electronic device may include a processing means (e.g., a central processing unit, a graphic processor, etc.) that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) or a program loaded from a storage means into a Random Access Memory (RAM). In the RAM, various programs and data necessary for the operation of the electronic apparatus are also stored. The processing device, the ROM, and the RAM are connected to each other by a bus. An input/output (I/O) interface is also connected to the bus.
Generally, the following systems may be connected to the I/O interface: input devices including, for example, touch screens, touch pads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, and the like; output devices including, for example, Liquid Crystal Displays (LCDs), speakers, vibrators, and the like; storage devices including, for example, magnetic tape, hard disk, etc.; and a communication device. The communication means may allow the electronic device to communicate wirelessly or by wire with other devices to exchange data. While the figures illustrate an electronic device with various systems, it is to be understood that not all illustrated systems are required to be implemented or provided. More or fewer systems may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means, or installed from a storage means, or installed from a ROM. The computer program, when executed by a processing device, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
The electronic device provided by the application adopts the earphone noise reduction method in the embodiment, and the technical problem of poor noise reduction effect of the earphone is solved. Compared with the prior art, the beneficial effects of the electronic device provided by the embodiment of the present application are the same as the beneficial effects of the earphone noise reduction method provided by the above embodiment, and other technical features in the electronic device are the same as the features disclosed in the above embodiment method, which are not repeated herein.
It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the foregoing description of embodiments, the particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
EXAMPLE five
The present embodiment provides a computer-readable storage medium having computer-readable program instructions stored thereon for performing the method for noise reduction of a headphone in the first embodiment.
The computer readable storage medium provided by the embodiments of the present application may be, for example, a usb disk, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or a combination of any of the above. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having 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. In the present embodiment, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device. Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer-readable storage medium may be embodied in an electronic device; or may be present alone without being incorporated into the electronic device.
The computer readable storage medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring initial sound signals picked up by each feedforward picking-up path, and detecting whether unsteady state noise signals exist in each initial sound signal; if the unsteady state noise signals exist, detecting the unsteady state noise amplitude corresponding to each initial sound signal; selecting a feedforward pickup signal from each initial sound signal according to each unsteady noise amplitude; and performing earphone noise reduction according to the feedforward pickup signal.
Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present disclosure may be implemented by software or hardware. Wherein the names of the modules do not in some cases constitute a limitation of the unit itself.
The computer-readable storage medium provided by the application stores computer-readable program instructions for executing the above earphone noise reduction method, and solves the technical problem of poor noise reduction effect of earphones. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the embodiment of the present application are the same as the beneficial effects of the noise reduction method for the earphone provided by the above embodiment, and are not described herein again.
Example six
The present application also provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of the above-described headphone noise reduction method.
The computer program product provided by the application solves the technical problem of poor noise reduction effect of the earphone. Compared with the prior art, the beneficial effects of the computer program product provided by the embodiment of the present application are the same as those of the noise reduction method for the earphone provided by the above embodiment, and are not described herein again.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.
Claims (10)
1. A method of headphone noise reduction, the headphone provided with a plurality of feed-forward pickup paths, the method comprising:
acquiring initial sound signals picked up by each feedforward picking-up path, and detecting whether unsteady state noise signals exist in each initial sound signal;
if the unsteady state noise signals exist, detecting the unsteady state noise amplitude corresponding to each initial sound signal;
selecting a feedforward pickup signal from each initial sound signal according to each unsteady noise amplitude;
and performing earphone noise reduction according to the feedforward pickup signal.
2. The headphone noise reduction method as defined in claim 1, wherein the step of selecting a feed-forward pickup signal in each of the initial sound signals based on each of the non-stationary noise amplitudes comprises:
if the amplitude of each unsteady state noise is larger than a preset noise amplitude threshold value, determining a first target amplitude with the minimum amplitude in each unsteady state noise amplitude;
taking the initial sound signal corresponding to the first target amplitude as the feedforward pickup signal;
if the unsteady state noise amplitudes are not all larger than the preset noise amplitude threshold, determining second target amplitudes smaller than the preset noise amplitude threshold in the unsteady state noise amplitudes;
and selecting the feedforward pickup signal from the initial sound signals corresponding to the second target amplitudes.
3. The method for reducing noise in a headphone as defined in claim 1, wherein the step of detecting the non-stationary noise amplitude corresponding to each of the initial acoustic signals comprises:
respectively extracting audio features corresponding to the initial sound signals;
and respectively inputting each audio characteristic into a preset signal amplitude detection model, and detecting the unsteady state noise amplitude corresponding to each initial sound signal.
4. The headphone noise reduction method as claimed in claim 3, wherein the preset signal amplitude detection model includes a first feature extractor,
the step of detecting the unsteady state noise amplitude corresponding to each initial sound signal by inputting each audio characteristic into a preset signal amplitude detection model respectively comprises:
extracting an unsteady noise amplitude feature in the audio features by inputting the audio features into the first feature extractor;
and determining the corresponding unsteady state noise amplitude according to the unsteady state noise amplitude characteristic.
5. The headphone noise reduction method according to any one of claims 1 to 4, wherein the step of detecting whether or not an unsteady noise signal exists in each of the initial sound signals comprises:
respectively carrying out feature recognition on the audio features of the initial sound signals according to a preset noise feature recognition model to obtain feature recognition results corresponding to the initial sound signals;
and determining whether the unsteady noise signal exists in each initial sound signal according to each feature recognition result.
6. The headphone noise reduction method according to claim 5, wherein the preset noise feature recognition model includes a second feature extractor, the feature recognition result includes a feature class label,
the step of respectively performing feature recognition on the audio features of the initial sound signals according to a preset noise feature recognition model to obtain feature recognition results corresponding to the initial sound signals comprises the following steps:
extracting non-stationary noise features in the audio features by inputting the audio features into the second feature extractor;
and classifying the unsteady noise characteristics to obtain the characteristic category label.
7. The headphone noise reduction method according to any one of claims 1 to 4, wherein the headphone noise reduction according to the feed-forward pickup signal comprises:
picking up the feedback signal through a feedback pick-up path of a headset;
generating a corresponding feedforward anti-phase noise signal according to the feedforward pickup signal, and generating a corresponding feedback anti-phase noise signal according to the feedback signal;
and carrying out earphone noise reduction according to the feedforward reversed-phase noise signal and the feedback reversed-phase noise signal.
8. A headphone noise reduction apparatus, wherein the headphone is provided with a plurality of feed-forward pickup paths, the headphone noise reduction apparatus comprising:
the first detection module is used for acquiring initial sound signals picked up by different feedforward pickup paths of the earphone and detecting whether unsteady state noise signals exist in the initial sound signals or not;
the second detection module is used for detecting the unsteady state noise amplitude corresponding to each initial sound signal if the unsteady state noise signal exists;
a selecting module, configured to select a feedforward pickup signal from each of the initial sound signals according to each of the unsteady noise amplitudes;
and the noise reduction module is used for performing earphone noise reduction according to the feedforward pickup signal.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the headphone noise reduction method of any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a program for implementing a headphone noise reduction method, the program being executed by a processor to implement the steps of the headphone noise reduction method according to any one of claims 1 to 7.
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