CN115348507A - Impulse noise suppression method, system, readable storage medium and computer equipment - Google Patents

Abstract

The invention provides an impulse noise suppression method, an impulse noise suppression system, a readable storage medium and computer equipment, wherein the method comprises the following steps: acquiring an audio signal of a current environment in real time, and extracting audio features of the audio signal to obtain a plurality of audio features of the audio signal; carrying out characteristic discrimination on each audio characteristic by using an audio database so as to identify pulse noise signals in the audio signals; judging whether the impulse noise signal is larger than a preset audio threshold value or not; if the impulse noise signal is larger than the preset audio threshold, inhibiting the impulse sample amplitude in the impulse noise signal by using an amplitude limiting function, and carrying out dynamic range companding on the processed impulse noise signal to obtain a primary impulse noise signal; and carrying out feedback elimination on the preliminary pulse noise signal so as to inhibit the pulse noise signal in the audio signal and obtain a target signal. The invention can inhibit the pulse noise signal in the audio signal so as to avoid the influence of the burst noise on the comfort level caused by the burst noise entering the human ear.

Description

Impulse noise suppression method, system, readable storage medium and computer equipment

technical field

The invention relates to the technical field of audio signal processing, in particular to an impulse noise suppression method, system, readable storage medium and computer equipment.

Background technique

With the rapid development of science and technology and the improvement of the comprehensive level of high-tech informationization, people's demand for video/audio services is growing exponentially. When people perform video/audio services, such as: voice calls, video calls, listening to music, etc., they usually wear earphones. As the usage rate of earphones increases, the audio optimization effect of earphones becomes more and more important.

When people wear headphones for video/audio services, if they are in a noisy environment, it will affect the comfort of using video/audio services. In the prior art, the sound in the environment is usually reduced The audio frequency is reduced, thereby reducing the impact of ambient sound on people's in-ear sound, so as to improve people's experience and comfort.

However, when faced with sudden noise, since the ambient noise reduction cannot recognize the sudden noise, the sudden noise will directly enter the ear, thereby affecting the user's experience and comfort.

Contents of the invention

Based on this, the object of the present invention is to provide an impulse noise suppression method, system, readable storage medium and computer equipment, so as to at least solve the above-mentioned deficiencies in the related art.

The present invention proposes a method for suppressing impulse noise, including:

Acquire the audio signal of the current environment in real time, and perform audio feature extraction on the audio signal to obtain multiple audio features of the audio signal;

Using the audio database to perform feature discrimination on each of the audio features to identify the impulse noise signal in the audio signal;

judging whether the impulse noise signal is greater than a preset audio threshold;

If the impulsive noise signal is greater than the preset audio threshold, a clipping function is used to suppress the pulse sample amplitude in the impulsive noise signal, and dynamic range companding is performed on the processed impulsive noise signal to obtain a preliminary impulsive noise signal ;

Feedback elimination is performed on the preliminary impulse noise signal to suppress the impulse noise signal in the audio signal to obtain a target signal.

Further, the step of performing audio feature extraction on the audio signal to obtain multiple audio features of the audio signal includes:

Acquiring audio parameters of the audio signal, and extracting multiple audio frame features in the audio signal based on the unit frame of the audio signal and the audio parameters;

Each of the audio frame features is sequentially processed through mean value, variance and standard deviation to obtain the audio segment feature corresponding to each of the audio frame features.

Further, the step of using the audio database to perform feature discrimination on each of the audio features to identify the impulse noise signal in the audio signal includes:

Obtain the sampling frequency of the audio signal and the data in each frame of each audio segment feature;

Calculate the short-term energy and the number of short-time zero crossings of the data in each frame according to the sampling frequency, and calculate the short-term energy, the number of short-term zero crossings and the short-term energy threshold and the number of zero crossings of the data in each frame The number threshold identifies impulsive noise signals in each of the audio segment features.

Further, the calculation formula of the short-term energy of the data in each frame is:

In the formula, En is the short-term energy, x(m) is the intra-frame data, w( _n ) is the window function, and N is the number of data frame samples corresponding to the sampling frequency.

Further, the formula for calculating the number of short-term zero-crossings of the data in each frame is:

w(n)为窗口函数，N为采样频率所对应的数据帧样点数。In the formula, Z _n is the short-term zero-crossing number, x(m) is the intra-frame data, sgn[ ] is the sign function, namely

w(n) is a window function, and N is the number of data frame samples corresponding to the sampling frequency.

Further, the step of identifying the impulsive noise signal in each of the audio segment features according to the short-term energy and the number of short-time zero-crossings and the short-term energy threshold and the number of zero-crossings of the data in each said frame includes:

When the short-term energy of the data in the frame is greater than or equal to the short-term energy threshold, and the number of short-term zero-crossings of the data in the frame is less than the threshold of the number of zero-crossings, it is determined that the data in the frame is a speech signal;

When the short-term energy of the intra-frame data is less than a short-term energy threshold and the number of short-term zero-crossings of the intra-frame data is greater than the zero-crossing threshold, it is determined that the intra-frame data is an impulse noise signal.

The present invention also proposes an impulse noise suppression system, including:

The audio signal acquisition module is used to acquire the audio signal of the current environment in real time, and perform audio feature extraction on the audio signal to obtain multiple audio features of the audio signal;

A feature discrimination module, configured to perform feature discrimination on each of the audio features using the audio database, so as to identify the impulse noise signal in the audio signal;

A judging module, configured to judge whether the impulse noise signal is greater than a preset audio threshold;

A noise signal suppression module, configured to use a clipping function to suppress the pulse sample amplitude in the pulse noise signal if the pulse noise signal is greater than a preset audio threshold, and perform dynamic range companding on the processed pulse noise signal , to get the preliminary impulse noise signal;

The feedback elimination module is configured to perform feedback elimination on the preliminary impulse noise signal to suppress the impulse noise signal in the audio signal to obtain a target signal.

Further, the audio signal acquisition module includes:

An audio parameter acquisition unit, configured to acquire audio parameters of the audio signal, and extract features of multiple audio frames in the audio signal based on the unit frame of the audio signal and the audio parameters;

The feature processing unit is configured to sequentially process each of the audio frame features through mean value, variance, and standard deviation to obtain the audio segment features corresponding to each of the audio frame features.

Further, the feature discrimination module includes:

A feature acquisition unit, configured to acquire the sampling frequency of the audio signal and each intra-frame data of each audio segment feature;

The feature discrimination unit is used to calculate the short-term energy and the number of short-term zero crossings of the data in each frame according to the sampling frequency, and calculate the short-term energy, the number of short-term zero crossings and the number of short-term zero crossings of the data in each frame The impulsive noise signal in each of the audio segment features is identified by using the temporal energy threshold and the zero crossing threshold.

Further, the feature discrimination unit is also used for:

When the short-term energy of the data in the frame is greater than or equal to the short-term energy threshold, and the number of short-term zero-crossings of the data in the frame is less than the threshold of the number of zero-crossings, it is determined that the data in the frame is a speech signal;

When the short-term energy of the intra-frame data is less than a short-term energy threshold and the number of short-term zero-crossings of the intra-frame data is greater than the zero-crossing threshold, it is determined that the intra-frame data is an impulse noise signal.

The present invention also proposes a readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned impulse noise suppression method is realized.

The present invention also proposes a computer device, comprising a memory, a processor, and a computer program stored on the memory and operable on the processor, and the above-mentioned impulse noise suppression is realized when the processor executes the computer program method.

Compared with the prior art, the beneficial effect of the present invention is: by acquiring the audio signal of the environment in real time, performing audio feature extraction on the audio signal, and performing feature discrimination on the extracted audio feature, to identify the pulse in the audio signal For the noise signal, the impulsive noise signal is identified by means of feature discrimination, so as to suppress it; specifically, when the impulsive noise signal is greater than the preset threshold, the limiting function is used to suppress the pulse sample amplitude, And dynamic range companding is carried out to improve the performance of pulse noise reduction. Further, the pulse noise signal is eliminated by feedback, thereby suppressing the pulse noise signal in the audio signal, so as to prevent sudden noise from entering the human ear and affecting comfort. .

Description of drawings

Fig. 1 is the flowchart of the impulse noise suppressing method in the first embodiment of the present invention;

Fig. 2 is the detailed flowchart of step S101 in Fig. 1;

FIG. 3 is a schematic diagram of audio segment features in the first embodiment of the present invention;

Fig. 4 is the detailed flowchart of step S102 in Fig. 1;

Fig. 5 is a comparison graph before and after impulse noise suppression in the first embodiment of the present invention;

FIG. 6 is a structural block diagram of the impulse noise suppression system in the second embodiment of the present invention;

Fig. 7 is a structural block diagram of a computer device in a third embodiment of the present invention.

Description of main component symbols:

The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings.

Detailed ways

In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the associated drawings. Several embodiments of the invention are shown in the drawings. However, the present invention can be embodied in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of the present invention will be thorough and complete.

It should be noted that when an element is referred to as being “fixed on” another element, it may be directly on the other element or there may be an intervening element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or intervening elements may also be present. The terms "vertical," "horizontal," "left," "right," and similar expressions are used herein for purposes of illustration only.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field of the invention. The terms used herein in the description of the present invention are for the purpose of describing specific embodiments only, and are not intended to limit the present invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items. , embodiment one

Please refer to FIG. 1, which shows the impulse noise suppression method in the first embodiment of the present invention, the method specifically includes steps S101 to S105:

S101, acquire an audio signal of the current environment in real time, and perform audio feature extraction on the audio signal, so as to obtain multiple audio features of the audio signal;

Further, please refer to FIG. 2, the step S101 specifically includes steps S1011-S1012:

S1011. Acquire audio parameters of the audio signal, and extract features of multiple audio frames in the audio signal based on a unit frame of the audio signal and the audio parameters;

S1012, sequentially process each of the audio frame features through mean value, variance, and standard deviation to obtain audio segment features corresponding to each of the audio frame features.

During specific implementation, in the identification process for different audio signals, because the audio signal has short-term characteristics, the unit frame of the audio signal is utilized, and the unit frame is usually 10～40ms (20ms in the present embodiment), Obtain the audio parameters of the audio signal, and use the above-mentioned unit frame and audio parameters to extract multiple audio frame features in the above-mentioned audio signal, the audio frame features include time-domain features, frequency-domain features and acoustic perception features:

The time domain feature utilizes the information of the audio signal in the time domain, which can be understood as the time on the horizontal axis and the audio signal on the vertical axis. The time information of the audio signal is described by the number of zero crossings, short-term energy, volume, autocorrelation coefficient, etc.

The number of zero crossings refers to the number of times the audio signal passes through zero in the process of going from positive to negative and from negative to positive. For example: voiced sounds have a lower zero-crossing number, and unvoiced sounds have a higher zero-crossing number (voiced sounds are voiced when the vocal cords vibrate during pronunciation, and voiceless sounds are not vibrated when the vocal cords are not vibrated).

The short-term energy is: it is used to monitor the conversion moment of voiced and unvoiced sounds through the calculation method of energy. In this embodiment, the number of zero crossings is low where the short-term energy is large, and the number of zero crossings is high where the short-term energy is small.

The frequency domain feature is to convert the time domain waveform signal into a frequency spectrum, and then perform calculations.

Further, the above audio frame features are sequentially processed through the mean value, variance and standard deviation, and then the audio segment features corresponding to each audio frame feature are obtained (see FIG. 3 for details).

S102, using the audio database to perform feature discrimination on each of the audio features, so as to identify the impulse noise signal in the audio signal;

Further, please refer to FIG. 4, the step S102 specifically includes steps S1021-S1022:

S1021. Obtain the sampling frequency of the audio signal and the intra-frame data of each audio segment feature;

S1022. Calculate the short-term energy and the number of short-term zero crossings of the data in each frame according to the sampling frequency, and calculate the short-term energy, the number of short-time zero crossings and the short-term energy threshold and A zero-crossing threshold identifies impulsive noise signals in each of said audio segment features.

In actual implementation, since the audio signal is a non-stationary signal with short-term stationary characteristics, most of the noise is steady-state, such as white noise/machine noise, and most of the energy in the audio signal is contained in the low-frequency band, while Noise signals usually have less energy and contain information in higher frequency bands;

Therefore, in this embodiment, by obtaining the sampling frequency of the above-mentioned audio signal and the data in each frame of each audio segment feature, wherein, in this embodiment, the sampling frequency is 8KHz, which means that each frame contains 160 samples The number of points, according to the sampling frequency and the corresponding number of samples, calculate the short-term energy of the data in each frame, which is the short-term energy of the audio signal, shift the number of samples in the frame by 1, and then calculate the product corresponding to two adjacent samples , where the point with a negative sign is the zero-crossing here, and the number of short-term zero-crossings of the frame is calculated by multiplying all the negative numbers in the frame. The calculation formula of the short-term energy of the data in each frame is:

In the formula, En is the short-term energy, x(m) is the intra-frame data, w( _n ) is the window function, and N is the number of data frame samples corresponding to the sampling frequency.

The calculation formula of the short-term zero-crossing number of the data in each said frame is:

w(n)为窗口函数，N为采样频率所对应的数据帧样点数。In the formula, Z _n is the short-term zero-crossing number, x(m) is the intra-frame data, sgn[ ] is the sign function, namely

w(n) is a window function, and N is the number of data frame samples corresponding to the sampling frequency.

Use the audio database to obtain the short-term energy threshold and the zero-crossing threshold of the impulse noise signal, and use the short-term energy threshold and the zero-crossing threshold of the impulse noise signal and the short-term energy and short-time of the data in each frame obtained above The number of zero crossings is used to identify the impulsive noise signal in the characteristics of each audio segment;

Specifically, when the short-term energy of the data in the frame is greater than or equal to the short-term energy threshold, and the number of short-term zero-crossings of the data in the frame is less than the threshold of the number of zero-crossings, it is determined that the data in the frame is a speech signal;

That is: when E _n ≥ STE threshold (short-term energy threshold), and Z _n < ZCC threshold (zero-crossing threshold), it is determined that the data in the frame is a speech signal;

When the short-term energy of the data in the frame is less than the short-term energy threshold and the number of short-term zero-crossings of the data in the frame is greater than the threshold of the number of zero-crossings, it is determined that the data in the frame is an impulse noise signal;

That is: when E _n <STE threshold (short-term energy threshold), and Z _n >ZCC threshold (zero-crossing threshold), it is determined that the data in the frame is an impulse noise signal.

S103, judging whether the impulse noise signal is greater than a preset audio threshold;

During specific implementation, it is judged whether the decibel of the above-mentioned impulse noise signal is greater than the preset audio threshold (in this embodiment, the preset audio threshold is 85dB), when the decibel of the impulse noise signal is greater than the preset audio threshold, it means The impulsive noise signal affects the auditory experience of the human ear, therefore, it is necessary to perform corresponding suppression processing on the impulsive noise signal;

When the decibel of the impulse noise signal is not greater than the preset audio threshold, it means that the impulse noise signal belongs to the tolerance range of the sound pressure in the ear canal. For the processing of the impulse noise signal, only the normal speech except the impulse noise signal needs to be The signal can be enhanced.

It should be noted that, in some optional embodiments, when the earphone is worn, the earplug of the earphone will perform a certain noise reduction function on external noise, usually the physical noise reduction can achieve a noise reduction effect of 15-20dB, so , when the headset has a physical noise reduction function, the preset audio threshold is 105dB.

S104. If the impulse noise signal is greater than the preset audio threshold, use a clipping function to suppress the pulse sample amplitude in the impulse noise signal, and perform dynamic range companding on the processed impulse noise signal to obtain a preliminary impulse noise signal;

In specific implementation, if the impulse noise signal is greater than the preset audio threshold, then use the clipping function to suppress the pulse sample amplitude in the impulse noise signal, and perform dynamic range companding on the processed impulse noise signal, so as to Obtain a preliminary impulse noise signal;

Specifically, input the audio signal, and set the window function type and window length, the STFT of the noisy signal, and use the time spectrogram, set the threshold for the first screening, select the window for possible impulse noise, set the correlation detection coefficient, and filter again, according to the formula Return the position of the noise in the time domain, and then output the processed audio signal through signal repair and reconstruction. The repair algorithm steps include setting the threshold band-pass limiter, the noisy signal through the limiter, setting the improved median filter parameters, threshold The filtered signal is reconstructed to obtain the reconstructed signal.

After the above-mentioned processed audio signal is obtained, the dynamic range of the audio signal is compressed or limited, and the relative variation range between the maximum level and the minimum level of the signal is reduced, so as to reduce distortion and noise. Effect.

In this embodiment, a noise estimation algorithm based on recursive averaging and a dynamic range companding algorithm are combined to propose a dynamic range expansion algorithm that dynamically adjusts the critical point of noise simulation. Through the dynamic evaluation of the noise level, the noise imitation threshold is adjusted in real time, and the high noise generated at the moment is further processed for noise reduction while the sound pickup is enhanced, so as to avoid the noise being amplified by the dynamic range companding algorithm while amplifying the voice. Amplify the problem.

S105. Perform feedback elimination on the preliminary impulse noise signal to suppress the impulse noise signal in the audio signal to obtain a target signal.

During specific implementation, the action instruction for eliminating the impulse noise signal is fed back to the above-mentioned preliminary impulse noise signal, and the super large signal in the preliminary impulse noise signal is subjected to blocking feedback processing, and then the impulse noise signal is suppressed or the impulse noise signal is eliminated.

Specifically, the gain output is controlled on the circuit to limit the preliminary impulse noise signal within the sound signal. Perform noise reduction processing for instantaneous high noise, control gain output, when the noise signal exceeds the set threshold value, automatically cut off the voice enhancement circuit, block the connection between the receiver and the power amplifier circuit, and delay until the end of the impact noise More than 200mS. At the same time, the microphone signal is processed, and the burst pulse signal is limited and smoothed to suppress the processing, so as to avoid transmitting large noise signals to the receiving end.

Please refer to Figure 5, which shows the test curve after impulse noise suppression, where L1 represents the audio signal curve of 0dB in the empty field (the noise environment is equivalent to the 0dB reference value), and L2 represents the noise reduction curve (that is, when wearing headphones The audio signal curve obtained), L3 represents the pickup enhancement curve, and L4 represents the audio signal curve after impulse noise suppression.

In summary, the impulsive noise suppression method in the above-mentioned embodiments of the present invention obtains the audio signal of the environment in real time, extracts audio features from the audio signal, and performs feature discrimination on the extracted audio features to identify the audio signal in the audio signal. For impulse noise signals, the impulse noise signals are identified by means of feature discrimination, so as to suppress them; specifically, when the impulse noise signals are greater than the preset threshold, the limiting function is used to suppress the pulse sample amplitude in the impulse noise signals , and perform dynamic range companding to improve the performance of pulse noise reduction. Further, the pulse noise signal is eliminated by feedback, thereby suppressing the pulse noise signal in the audio signal, so as to avoid sudden noise entering the human ear and affecting comfort Spend.

Embodiment two

Another aspect of the present invention also proposes an impulse noise suppression system, please refer to Figure 6, which shows the impulse noise suppression system in the second embodiment of the present invention, the system includes:

The audio signal acquisition module 11 is used to obtain the audio signal of the current environment in real time, and perform audio feature extraction on the audio signal to obtain multiple audio features of the audio signal;

Further, the audio signal acquisition module 11 includes:

An audio parameter acquisition unit, configured to acquire audio parameters of the audio signal, and extract features of multiple audio frames in the audio signal based on the unit frame of the audio signal and the audio parameters;

The feature processing unit is configured to sequentially process each of the audio frame features through mean value, variance, and standard deviation to obtain the audio segment features corresponding to each of the audio frame features.

The feature discrimination module 12 is used to utilize the audio database to perform feature discrimination on each of the audio features, so as to identify the impulse noise signal in the audio signal;

Further, the feature discrimination module 12 includes:

A feature acquisition unit, configured to acquire the sampling frequency of the audio signal and each intra-frame data of each audio segment feature;

The feature discrimination unit is used to calculate the short-term energy and the number of short-term zero crossings of the data in each frame according to the sampling frequency, and calculate the short-term energy, the number of short-term zero crossings and the number of short-term zero crossings of the data in each frame The impulsive noise signal in each of the audio segment features is identified by using the temporal energy threshold and the zero crossing threshold.

In some optional embodiments, the feature discrimination unit is also used for:

When the short-term energy of the data in the frame is greater than or equal to the short-term energy threshold, and the number of short-term zero-crossings of the data in the frame is less than the threshold of the number of zero-crossings, it is determined that the data in the frame is a speech signal;

When the short-term energy of the intra-frame data is less than a short-term energy threshold and the number of short-term zero-crossings of the intra-frame data is greater than the zero-crossing threshold, it is determined that the intra-frame data is an impulse noise signal.

A judging module 13, configured to judge whether the impulse noise signal is greater than a preset audio threshold;

The noise signal suppression module 14 is used to suppress the pulse sample amplitude in the pulse noise signal by using a clipping function if the pulse noise signal is greater than a preset audio threshold, and perform dynamic range compression on the processed pulse noise signal. Expand to get the preliminary impulse noise signal;

The feedback elimination module 15 is configured to perform feedback elimination on the preliminary impulse noise signal to suppress the impulse noise signal in the audio signal to obtain a target signal.

The functions or operation steps realized by the above-mentioned modules and units when executed are substantially the same as those of the above-mentioned method embodiments, and will not be repeated here.

The impulsive noise suppression system provided by the embodiment of the present invention has the same realization principle and technical effect as the foregoing method embodiment. For the sake of brief description, for the parts not mentioned in the device embodiment, please refer to the corresponding content in the foregoing method embodiment. .

Embodiment Three

The present invention also proposes a computer device, please refer to FIG. 7, which shows a computer device in the third embodiment of the present invention, including a memory 10, a processor 20, and a computer stored on the memory 10 and can be stored on the processor. A computer program 30 running on the processor 20, the processor 20 implements the above-mentioned impulse noise suppression method when executing the computer program 30.

Wherein, the memory 10 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (eg, SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. Memory 10 may in some embodiments be an internal storage unit of a computer device, such as a hard disk of the computer device. In other embodiments, the memory 10 may also be an external storage device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash memory card (Flash Card) and the like. Further, the memory 10 may also include both an internal storage unit of the computer device and an external storage device. The memory 10 can be used not only to store application software and various data installed in the computer device, but also to temporarily store data that has been output or will be output.

Wherein, the processor 20 in some embodiments may be an electronic control unit (Electronic Control Unit, referred to as ECU, also known as a trip computer), a central processing unit (Central Processing Unit, CPU), a controller, a microcontroller, a microprocessor or Other data processing chips are used to run the program codes stored in the memory 10 or process data, such as executing access restriction programs and the like.

It should be noted that the structure shown in FIG. 7 does not constitute a limitation to the computer device. In other embodiments, the computer device may include fewer or more components than those shown in the illustration, or combine certain components, or be different layout of the components.

The embodiment of the present invention also provides a readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned impulse noise suppression method is realized.

Those skilled in the art will understand that the logic and/or steps shown in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, which can be specifically implemented in on any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, apparatus, or device and execute them), or Used in conjunction with these instruction execution systems, devices or equipment. For the purposes of this specification, a "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use in or in conjunction with an instruction execution system, device or device.

More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection with one or more wires (electronic device), portable computer disk case (magnetic device), random access memory (RAM), Read Only Memory (ROM), Erasable and Editable Read Only Memory (EPROM or Flash Memory), Fiber Optic Devices, and Portable Compact Disc Read Only Memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program can be printed, since the program can be read, for example, by optically scanning the paper or other medium, followed by editing, interpretation or other suitable processing if necessary. processing to obtain the program electronically and store it in computer memory.

It should be understood that various parts of the present invention can be realized by hardware, software, firmware or their combination. In the embodiments described above, various steps or methods may be implemented by software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one or combination of the following techniques known in the art: Discrete logic circuits, ASICs with suitable combinational logic gates, Programmable Gate Arrays (PGAs), Field Programmable Gate Arrays (FPGAs), etc.

The various technical features of the above-mentioned embodiments can be combined arbitrarily. To make the description concise, all possible combinations of the various technical features in the above-mentioned embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, should be considered as within the scope of this specification.

The above-mentioned embodiments only represent several implementation modes of the present application, and the description thereof is relatively specific and detailed, but it should not be construed as limiting the scope of the patent for the invention. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the scope of protection of the patent application should be based on the appended claims.

Claims (10)

1. A method for suppressing impulse noise, characterized in that, comprising: Acquire the audio signal of the current environment in real time, and perform audio feature extraction on the audio signal to obtain multiple audio features of the audio signal; Using the audio database to perform feature discrimination on each of the audio features to identify the impulse noise signal in the audio signal; judging whether the impulse noise signal is greater than a preset audio threshold; If the impulsive noise signal is greater than the preset audio threshold, a clipping function is used to suppress the pulse sample amplitude in the impulsive noise signal, and dynamic range companding is performed on the processed impulsive noise signal to obtain a preliminary impulsive noise signal ; Feedback elimination is performed on the preliminary impulse noise signal to suppress the impulse noise signal in the audio signal to obtain a target signal. 2. Impulse noise suppression method according to claim 1, is characterized in that, carrying out audio feature extraction to described audio signal, to obtain the step of a plurality of audio feature of described audio signal comprises: Acquiring audio parameters of the audio signal, and extracting multiple audio frame features in the audio signal based on the unit frame of the audio signal and the audio parameters; Each of the audio frame features is sequentially processed through mean value, variance and standard deviation to obtain the audio segment feature corresponding to each of the audio frame features. 3. The impulse noise suppressing method according to claim 2, wherein, utilizing the audio database to carry out feature discrimination to each of the audio features, to identify the step of the impulse noise signal in the audio signal comprising: Obtain the sampling frequency of the audio signal and the data in each frame of each audio segment feature; Calculate the short-term energy and the number of short-time zero crossings of the data in each frame according to the sampling frequency, and calculate the short-term energy, the number of short-term zero crossings and the short-term energy threshold and the number of zero crossings of the data in each frame The number threshold identifies impulsive noise signals in each of the audio segment features. 4. impulse noise suppression method according to claim 3, is characterized in that, the computing formula of the short-term energy of data in each described frame is:

In the formula, En is the short-term energy, x(m) is the intra-frame data, w( _n ) is the window function, and N is the number of data frame samples corresponding to the sampling frequency. 5. impulse noise suppressing method according to claim 3, is characterized in that, the calculation formula of the short-term zero-crossing number of data in each described frame is:

In the formula, Z _n is the short-term zero-crossing number, x(m) is the intra-frame data, sgn[ ] is the sign function, namely

w(n) is a window function, and N is the number of data frame samples corresponding to the sampling frequency. 6. The impulse noise suppression method according to claim 3, characterized in that, according to the short-term energy and the number of short-time zero-crossings and the short-term energy threshold and the number of zero-crossings of the data in each said frame, each said The steps for impulsive noise signals in audio segment features include: When the short-term energy of the data in the frame is greater than or equal to the short-term energy threshold, and the number of short-term zero-crossings of the data in the frame is less than the threshold of the number of zero-crossings, it is determined that the data in the frame is a speech signal; When the short-term energy of the intra-frame data is less than a short-term energy threshold and the number of short-term zero-crossings of the intra-frame data is greater than the zero-crossing threshold, it is determined that the intra-frame data is an impulse noise signal. 7. An impulse noise suppression system, comprising: The audio signal acquisition module is used to acquire the audio signal of the current environment in real time, and perform audio feature extraction on the audio signal to obtain multiple audio features of the audio signal; A feature discrimination module, configured to perform feature discrimination on each of the audio features using the audio database, so as to identify the impulse noise signal in the audio signal; A judging module, configured to judge whether the impulse noise signal is greater than a preset audio threshold; A noise signal suppression module, configured to use a clipping function to suppress the pulse sample amplitude in the pulse noise signal if the pulse noise signal is greater than a preset audio threshold, and perform dynamic range companding on the processed pulse noise signal , to get the preliminary impulse noise signal; The feedback elimination module is configured to perform feedback elimination on the preliminary impulse noise signal to suppress the impulse noise signal in the audio signal to obtain a target signal. 8. Impulse noise suppression system according to claim 7, is characterized in that, described audio signal acquisition module comprises: An audio parameter acquisition unit, configured to acquire audio parameters of the audio signal, and extract features of multiple audio frames in the audio signal based on the unit frame of the audio signal and the audio parameters; The feature processing unit is configured to sequentially process each of the audio frame features through mean value, variance, and standard deviation to obtain the audio segment features corresponding to each of the audio frame features. 9. A readable storage medium, on which a computer program is stored, wherein when the computer program is executed by a processor, the impulse noise suppression method according to any one of claims 1 to 6 is realized. 10. A computer device, comprising a memory, a processor, and a computer program stored on the memory and operable on the processor, characterized in that, when the processor executes the computer program, the computer program according to claim The impulse noise suppression method described in any one of 1 to 6.

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