CN114822481A - Method and system for canceling and reducing acoustic noise by magnetic resonance sound wave - Google Patents
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Abstract
The invention provides a method and a system for canceling and reducing acoustic noise by magnetic resonance sound waves, wherein the method comprises the following steps: deploying an array of acoustic noise sensors at the magnetic resonance acoustic noise source, and sampling and quantizing an initial magnetic resonance acoustic noise analog signal through the acoustic noise sensors to establish a primary sound channel; x after sampling and quantization [n] Storing the samples by amplitude values; extraction of x [n] The characteristic signal comprises amplitude and phase; converting the characteristic signal into an electric signal as an input reference signal of the self-adaptive filter for filtering, outputting a cancellation electric signal to establish a secondary sound channel, and driving a cancellation loudspeaker to generate a cancellation sound field in space; and acquiring residual signals in real time by the secondary sound channel to perform feedback adjustment, and reducing the magnetic resonance acoustic noise to be below a preset threshold value after multiple iterations. The invention reduces the sound pressure by superposition of the primary and secondary sound fields, designs a corresponding sound signal extraction method and a self-adaptive filtering algorithm, and extracts the dynamic state of the sound noise characteristic signalAnd the efficiency of the acoustic noise is greatly improved.
Description
Technical Field
The invention relates to the technical field of medical treatment, in particular to a method and a system for canceling and reducing acoustic noise by using magnetic resonance sound waves.
Background
The existing nuclear magnetic resonance high-power device brings great sound noise, and the noise generated by magnetic resonance examination is mainly because the gradient field is continuously switched. The noise is transmitted to the ears of the examiners because the noise generated by the gradient field during switching can be transmitted through air and solid (scanning machine), and when the noise source generates noise, the parts of the scanning machine are affected to generate vibration and further generate sound, and the sound also enters the ears of the examiners to greatly affect the examiners, and even some people with low tolerance can crash. In the prior art, sound insulation means such as earplugs, earmuffs and the like are adopted, so that the load of inspectors is added, the inspectors feel tired, and the effect of reducing sound noise is poor.
Sound is essentially a change in air pressure (called sound pressure), and the change in air pressure is essentially a change in the macroscopic appearance of thermal motion due to changes in the density of air molecules. In the linear acoustic category, sound propagates through the form of waves.
In fact, the sound in daily life is generally a non-stationary signal, the statistical characteristics of which are not fixed, and the same is true of the acoustic noise in the mri scan, and the primary sound field (noise source) in the mri scan varies with time.
Although the existing magnetic resonance noise reduction technology includes a comb filter noise filtering technology, a noise cancellation technology and the like, wherein the comb filter noise filtering technology has a certain attenuation on noise sound pressure, the method starts from the repeatability of gradient magnetic field switching, makes an ideal setting, and is based on a periodic scanning sequence, so that a lot of aperiodic noise signals are leaked. The basic principle of the existing noise cancellation technology is to use a spectral subtraction method of reference noise to perform cancellation operation on a noise-polluted signal and a reference signal aiming at low signal-to-noise ratio and strong impact of gradient noise, however, the existing noise cancellation only focuses on the iterative convergence process of the cancellation operation method, and does not relate to the extraction of the acoustic noise signal characteristics of a noise source, and the extraction and tracking of the noise source signal characteristics are directly related to the final cancellation and noise reduction effect.
Disclosure of Invention
In view of this, the present invention provides a method design for actively reducing noise by sound wave cancellation, which extracts dynamic changes of a characteristic signal of sound noise, generates a secondary sound field in a space, tracks and changes the secondary sound field, and keeps an optimal noise reduction state, so that the sound wave generated by the secondary sound field is equal to the primary sound wave in amplitude and opposite in phase within a certain range of the space, and the superposition of the primary and secondary sound fields reduces the sound pressure in the place or the area, thereby achieving the purpose of sound noise cancellation, significantly improving the efficiency of sound noise reduction, and improving the sensitivity of people.
The invention provides a method for canceling and reducing acoustic noise by using magnetic resonance sound waves, which comprises the following steps:
s1, deploying an array of acoustic noise sensors at the magnetic resonance acoustic noise source, and sampling and quantizing the initial magnetic resonance acoustic noise analog signals through the acoustic noise sensors to establish a primary sound channel;
the sampled and quantized waveform is denoted x [n] Where n is a time index; storing the sampled samples as amplitude values;
s2, extracting x [n] The characteristic signal of the magnetic resonance acoustic noise comprises amplitude and phase;
the extraction x [n] The method for the characteristic signal of the magnetic resonance acoustic noise comprises the following steps:
A. for x [n] Pre-emphasis is carried out on the high-frequency part of the magnetic resonance acoustic noise, so that the loss of the high-frequency part is made up, and the integrity of the sound channel information is protected;
the pre-emphasis method is to pre-emphasize x by a high-pass filter [n] High frequency energy of (a), high pass filter denoted as y [n] ＝x [n] -ax [n -1]A is a filter coefficient;
the energy of the high-frequency part of the sound is increased, for the frequency spectrum of the sound signal, the energy of the low-frequency part is usually higher than that of the high-frequency part, the frequency spectrum energy is attenuated by 20dB every time when 10 times of Hz is passed, and the energy of the low-frequency part is also increased due to the influence of circuit background noise when the sound noise sensor collects the sound signal, so that the energy of the high-frequency part and the energy of the low-frequency part have similar amplitude, and the high-frequency energy of the collected sound needs to be pre-enhanced. The energy of the high-frequency part is strengthened, so that the acoustic model can better utilize the high-frequency formants, and the identification accuracy is improved;
B. for x [n] Windowing the characteristic signal of the magnetic resonance acoustic noise;
the sound in daily life is generally a non-stationary signal, the statistical properties of which are not fixed, but over a relatively short period of time, the signal can be considered stationary, which is windowing. The window is described by three parameters: window length (in milliseconds), offset, and shape; each windowed sound signal is called a frame, the millisecond number of each frame is called a frame length, and the distance between the left boundaries of two adjacent frames is called a frame shift;
from the characteristic signal s [n] The process of extracting one frame is denoted as y [n] ＝w [n] s [n] Let a w [n] For rectangular windows, the feature signal is cut off at the boundary, and these discontinuities will affect the fourier analysis, so in the MFCC algorithm, windowing uses a hamming window with edges smoothly dropping to 0, and the expression is as follows:
in the formula (1), L is the frame length;
C. extracting discrete frequency band spectrum information from each windowed frame characteristic signal;
the method for extracting the frequency spectrum information of the discrete frequency band adopts Discrete Fourier Transform (DFT), and the expression is as follows:
in the formula (2), the input of DFT is x [n] The output of each windowed signal is a complex number X comprising N frequency bands [k] ，X [k] Representing the original signal x [n] The amplitude and phase of a certain frequency component;
calculating expression (2) of DFT by fast fourier transform FFT, N being a power of 2;
D. the FFT result contains the energy information of the frame signal in each frequency band; however, the sensitivity of human auditory sense to different frequency bands is different, the human auditory sense to high frequency is not as sensitive as to low frequency, the boundary line is about 1000Hz, and the characteristic of simulating human auditory sense when extracting sound features can improve the recognition performance. The practice in MFCC is to map the frequency of the DFT output to the mel scale, where one mel is a pitch unit, where sounds perceived as equidistant can be separated by the same number of mel numbers [18], the correspondence between frequency (in Hz) and mel scale is linear below 1000Hz and logarithmic above 1000Hz, and the formula of the frequency of the DFT output to the mel scale is as follows:
in formula (3), f is frequency in Hz;
mixing X [k] The FFT spectrum is converted into Mel spectrum through a Mel filter bank with M filters, the Mel filter bank is generally a set of Mel-scale triangular filter bank, 10 filters below 1000Hz are linearly separated, the rest filters above 1000Hz are logarithmically separated, the adopted Mel filter is a triangular filter, and the center frequency is f (m) Where M is 1, 2, …, M is usually 22-26 (the number of filters is close to the number of critical bands), the interval between f (M) decreases with decreasing M and increases with increasing M, and the frequency of each triangular filter is expressed by:
in the formula (4), the reaction mixture is,
by using the triangular band-pass filter, the frequency spectrum can be smoothed, the effect of harmonic waves can be eliminated, and the formants of the original sound can be highlighted. Therefore, the tone or pitch of a segment of sound is not reflected in the MFCC parameters, that is, the MFCC is taken as the acoustic feature, and the recognition result is not affected by the difference of the tone of the input sound. In addition, the amount of calculation can be reduced.
After the mel frequency spectrum is obtained, calculating the logarithmic energy output by each mel filter bank; generally, the response of a person to sound pressure is logarithmic, the sensitivity of the person to small changes of high sound pressure is not as good as that of low sound pressure, and in addition, the sensitivity of the extracted features to the changes of input sound energy can be reduced by using the logarithm, because the distance between the sound and the acoustic noise sensor is changed, so the sound energy collected by the acoustic noise sensor is also changed, and the logarithmic energy output by each mel filter is:
E. the mel frequency spectrum is transformed back to a time domain signal, and by utilizing the logarithmic energy of the mel filter, the cepstrum coefficient is obtained by discrete cosine transform:
in the formula (6), L denotes the MFCC order, and usually 12 orders can represent acoustic features; m indicates the number of the triangular filters;
F. for x [n] Performing energy and difference on the characteristic signals of the magnetic resonance acoustic noise; the energy of a frame is defined as the sum of the squares of the sample points of a frame, and for a windowed signal x, the energy from sample point t1 to sample point t2 is:
the above extracted feature signals are considered individually for each frame and are static, while the actual sound is continuous, and there is a relation between frames, so that features need to be added to represent the dynamic change between frames, which is usually realized by calculating the first order difference or even the second order difference of 13 features (12 cepstral features plus 1 energy) for each frame. One simple method for calculating the difference is to calculate the difference between 13 features of each frame before and after the current frame:
in the formula (8), c (t +1) is a frame next to the current frame, and c (t-1) is a frame before the current frame;
if the second order difference is not considered, the MFCC characteristics for each frame are finally 26 dimensions: 12-dimensional cepstrum coefficients, 12-dimensional cepstrum coefficient differences, 1-dimensional energy and 1-dimensional energy differences;
s3, converting the characteristic signal into an electric signal as an input reference signal of a self-adaptive filter, carrying out self-adaptive filtering processing on the input reference signal, outputting a cancellation electric signal to establish a secondary sound channel, and driving a cancellation loudspeaker to generate a cancellation sound field in space; and the secondary sound channel acquires residual signals in real time to perform feedback adjustment, and reduces the magnetic resonance acoustic noise to be below a preset threshold value after multiple iterations.
Further, the adaptive filtering processing method in step S3 includes deploying an array of error sensors at a location of the magnetic resonance apparatus where the acoustic noise needs to be reduced, monitoring the cancellation condition of the acoustic noise, and feeding the error signal back to the adaptive filter to adjust the weight of the adaptive filter, so as to control the cancellation sound field to minimize the sum of mean square of the error signal.
Further, the method for adjusting the weight values of the adaptive filter in step S3 includes:
and (3) iteratively calculating the weight value of the adaptive filter, wherein the expression is as follows:
W a (j+1)＝W a (j)+NS(j-n-K)e(j) (9)
w in formula (9) a (j) The weight of the nth branch of the adaptive filter at the sampling moment j;
s (j) input signal sample values;
e (j) error signal sample values;
n is a parameter for controlling stability and convergence rate;
k-acoustic delay, K must be equal to the feedback delay of out (t) to e (t) over the entire frequency band of interest.
In acoustic noise cancellation, unlike pure electrical noise cancellation, the transfer functions of the acousto-electric, electro-acoustic sensors and the transfer function of the spatial propagation of acoustic noise, in particular the acoustic delay, must be considered. Since the sound waves emitted by the secondary source are not immediately received by the error sensor, but rather undergo an acoustic delay of K, the effect of this acoustic delay on the system is crucial.
Further, the method for driving the cancellation loudspeaker to generate the cancellation sound field in the space in step S3 includes that the cancellation electric signal is connected to the cancellation loudspeaker after D/a conversion and analog power amplification, and the cancellation sound wave generated by the cancellation loudspeaker is superimposed with the magnetic resonance sound noise in the local space near the acoustic noise sensor array, so as to weaken the influence of the magnetic resonance sound signal.
Further, the method for controlling the cancellation field to minimize the sum of mean square of error signals comprises: the RVSS-FXLMS structure is adopted, based on the filtering type minimum mean square FXLMS, and the variable convergence factor RVSS algorithm is matched to increase the convergence speed and stability of the FXLMS according to the control state.
Further, the construction method of the RVSS-FXLMS framework comprises the following steps: in an active acoustic noise control mode, a self-adaptive filter generates a feedforward reverse electric signal to inhibit a focusing error caused by a low-frequency signal.
The invention also provides a system for canceling the sound and reducing the noise by using the magnetic resonance sound wave, which executes the method for canceling the sound and reducing the noise by using the magnetic resonance sound wave, and comprises the following steps:
a sampling and quantization module: an array of acoustic noise sensors is deployed at the source of the magnetic resonance acoustic noise, by which the initial magnetic resonance acoustic noise analog signal is sampled and quantized, creating a primary channel.
A characteristic signal extraction module: for extracting x [n] The characteristic signal of the magnetic resonance acoustic noise comprises amplitude and phase; for x [n] Pre-emphasis is performed on the high-frequency part of the magnetic resonance acoustic noise; for x [n] Windowing the characteristic signal of the magnetic resonance acoustic noise; extracting discrete frequency band spectrum information from each windowed frame characteristic signal; corresponding the frequency output by DFT to mel scale, and calculating the logarithmic energy output by each mel filter bank after acquiring mel frequency spectrum; the mel frequency spectrum is converted back to a time domain signal, and a cepstrum coefficient is obtained by discrete cosine transform by utilizing the logarithmic energy of a mel filter; for x [n] Performing energy and difference on the characteristic signals of the magnetic resonance acoustic noise;
the active cancellation and noise reduction module: the device is used for converting the characteristic signal into an electric signal serving as an input reference signal of a self-adaptive filter, performing self-adaptive filtering processing on the input reference signal, outputting a cancellation electric signal to establish a secondary sound channel, and driving a cancellation loudspeaker to generate a cancellation sound field in space; and the secondary sound channel acquires residual signals in real time to perform feedback adjustment, and reduces the magnetic resonance acoustic noise to be below a preset threshold value after multiple iterations.
Further, the active cancellation and noise reduction module includes:
a secondary channel sub-module: the cancellation signal is accessed to the cancellation loudspeaker after D/A conversion and analog power amplification, and cancellation sound waves generated by the cancellation loudspeaker are superposed with magnetic resonance sound noise in a local space near the sound noise sensor array, so that the influence of the magnetic resonance sound signals is weakened;
an error signal feedback sub-module: the method is used for monitoring the acoustic noise cancellation condition and feeding an error signal back to the adaptive filter to adjust the weight of the adaptive filter, so that the cancellation field is controlled to minimize the mean square sum of the error signal.
The present invention also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of the above-mentioned method for canceling acoustic noise by magnetic resonance acoustic wave.
The invention also provides a computer device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to realize the steps of the method for canceling acoustic noise by using magnetic resonance sound wave.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, the secondary sound field is generated in the space, so that the primary sound field and the secondary sound field are superposed to reduce the sound pressure of the area so as to eliminate the sound noise, a corresponding sound signal extraction method and a self-adaptive filtering algorithm are designed, the dynamic change of the sound noise characteristic signal is extracted, the sound noise reduction efficiency is greatly improved, and the sensitivity of personnel is improved.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention.
In the drawings:
FIG. 1 is a flow chart of a method for canceling acoustic noise by using magnetic resonance sound waves according to the present invention;
FIG. 2 is a schematic diagram of a computer device according to an embodiment of the present invention;
FIG. 3 shows the extraction of x according to an embodiment of the present invention [n] A flow chart of a characteristic signal of magnetic resonance acoustic noise.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. The following description refers to the accompanying drawings in which the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and products consistent with certain aspects of the present disclosure, as detailed in the appended claims.
The terminology used in the present disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used in this disclosure and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present disclosure. The word "if," as used herein, may be interpreted as "at … …" or "when … …" or "in response to a determination," depending on the context.
The embodiments of the present invention will be described in further detail with reference to the accompanying drawings.
The embodiment of the invention provides a method for canceling and reducing acoustic noise by using magnetic resonance sound waves, which is shown in figure 1 and comprises the following steps:
s1, deploying an array of acoustic noise sensors at the magnetic resonance acoustic noise source, and sampling and quantizing the initial magnetic resonance acoustic noise analog signals through the acoustic noise sensors to establish a primary sound channel;
the sampled and quantized waveform is denoted x [n] Where n is a time index; storing the sampled samples as amplitude values;
s2, extracting x [n] The characteristic signal of the magnetic resonance acoustic noise comprises amplitude and phase;
the extraction x [n] A method for characterizing signals of magnetic resonance acoustic noise, as shown in fig. 3, comprising the steps of:
A. for x [n] Pre-emphasis is carried out on the high-frequency part of the magnetic resonance acoustic noise, so that the loss of the high-frequency part is made up, and the integrity of the sound channel information is protected;
the pre-emphasis method is to pre-emphasize x by a high-pass filter [n] High frequency energy of (2), high pass filter denoted as y [n] ＝x [n] -ax [n-1] A is a filter coefficient;
the energy of the high-frequency part of the sound is increased, for the frequency spectrum of the sound signal, the energy of the low-frequency part is usually higher than that of the high-frequency part, the frequency spectrum energy is attenuated by 20dB every time when 10 times of Hz is passed, and the energy of the low-frequency part is also increased due to the influence of circuit background noise when the sound noise sensor collects the sound signal, so that the energy of the high-frequency part and the energy of the low-frequency part have similar amplitude, and the high-frequency energy of the collected sound needs to be pre-enhanced. The energy of the high-frequency part is strengthened, so that the acoustic model can better utilize the high-frequency formants, and the identification accuracy is improved;
B. for x [n] Windowing the characteristic signal of the magnetic resonance acoustic noise;
the sound in daily life is generally a non-stationary signal, the statistical properties of which are not fixed, but over a relatively short period of time, the signal can be considered stationary, which is windowing. The window is described by three parameters: window length (in milliseconds), offset, and shape; each windowed sound signal is called a frame, the millisecond number of each frame is called a frame length, and the distance between the left boundaries of two adjacent frames is called frame shift;
from the characteristic signal s [n] The process of extracting one frame is denoted as y [n] ＝w [n] s [n] Let a w [n] For rectangular windows, the feature signal is cut off at the boundary, and these discontinuities will affect the fourier analysis, so in the MFCC algorithm, windowing uses a hamming window with edges smoothly dropping to 0, and the expression is as follows:
in the formula (1), L is the frame length;
C. extracting discrete frequency band spectrum information from each windowed frame characteristic signal;
the method for extracting the frequency spectrum information of the discrete frequency band adopts Discrete Fourier Transform (DFT), and the expression is as follows:
in the formula (2), the input of DFT is x [n] The output of each windowed signal is a complex number X comprising N frequency bands [k] ，X [k] Representing the original signal x [n] The amplitude and phase of a certain frequency component;
calculating expression (2) of DFT by fast fourier transform FFT, N being a power of 2;
D. the FFT result contains the energy information of the frame signal in each frequency band; however, the sensitivity of human auditory sense to different frequency bands is different, the human auditory sense to high frequency is not as sensitive as to low frequency, the boundary line is about 1000Hz, and the characteristic of simulating human auditory sense when extracting sound features can improve the recognition performance. The practice in MFCC is to map the frequency of the DFT output to the mel scale, where one mel is a pitch unit, where sounds perceived as equidistant can be separated by the same number of mel numbers [18], the correspondence between frequency (in Hz) and mel scale is linear below 1000Hz and logarithmic above 1000Hz, and the formula of the frequency of the DFT output to the mel scale is as follows:
in formula (3), f is frequency in Hz;
mixing X [k] The FFT spectrum is converted into Mel spectrum through a Mel filter bank with M filters, the Mel filter bank is generally a set of Mel-scale triangular filter bank, 10 filters below 1000Hz are linearly separated, the rest filters above 1000Hz are logarithmically separated, the adopted Mel filter is a triangular filter, and the center frequency is f (m) Where M is 1, 2, …, M is usually 22-26 (the number of filters is close to the critical band number), the interval between f (M) decreases with decreasing M value and increases with increasing M value, and the frequency of each triangular filter is expressed by:
in the formula (4), the reaction mixture is,
by using the triangular band-pass filter, the frequency spectrum can be smoothed, the effect of harmonic waves can be eliminated, and the formants of the original sound can be highlighted. Therefore, the tone or pitch of a segment of sound is not reflected in the MFCC parameters, that is, the MFCC is taken as the acoustic feature, and the recognition result is not affected by the difference of the tone of the input sound. In addition, the amount of calculation can be reduced.
After the mel frequency spectrum is obtained, calculating the logarithmic energy output by each mel filter bank; generally, the response of a person to sound pressure is logarithmic, the sensitivity of the person to small changes of high sound pressure is not as good as that of low sound pressure, and in addition, the sensitivity of the extracted features to the changes of input sound energy can be reduced by using the logarithm, because the distance between the sound and the acoustic noise sensor is changed, so the sound energy collected by the acoustic noise sensor is also changed, and the logarithmic energy output by each mel filter is:
E. the mel frequency spectrum is transformed back to a time domain signal, and by utilizing the logarithmic energy of the mel filter, the cepstrum coefficient is obtained by discrete cosine transform:
in the formula (6), L denotes the MFCC order, and usually 12 orders can represent acoustic features; m indicates the number of the triangular filters;
F. for x [n] Performing energy and difference on the characteristic signals of the magnetic resonance acoustic noise; the energy of a frame is defined as the sum of the squares of the sample points of a frame, and for a windowed signal x, the energy from sample point t1 to sample point t2 is:
the above extracted feature signals are considered separately for each frame, and are static, while the actual sound is continuous, and there is a relation between frames, so that features need to be added to represent the dynamic change between frames, which is usually realized by calculating the first order difference or even the second order difference of 13 features (12 cepstral features plus 1 energy) for each frame. One simple method for calculating the difference is to calculate the difference between 13 features of each frame before and after the current frame:
in the formula (8), c (t +1) is a frame next to the current frame, and c (t-1) is a frame previous to the current frame;
if the second order difference is not considered, the MFCC characteristics for each frame are finally 26 dimensions: 12-dimensional cepstrum coefficients, 12-dimensional cepstrum coefficient differences, 1-dimensional energy and 1-dimensional energy differences;
s3, converting the characteristic signal into an electric signal as an input reference signal of a self-adaptive filter, carrying out self-adaptive filtering processing on the input reference signal, outputting a cancellation electric signal to establish a secondary sound channel, and driving a cancellation loudspeaker to generate a cancellation sound field in space; and the secondary sound channel acquires residual signals in real time to perform feedback adjustment, and reduces the magnetic resonance acoustic noise to be below a preset threshold value after multiple iterations.
Preferably, the adaptive filtering processing method in step S3 includes deploying an array of error sensors at a location of the magnetic resonance apparatus where the acoustic noise needs to be reduced, monitoring the cancellation condition of the acoustic noise, and feeding the error signals back to the adaptive filter to adjust the weights of the adaptive filter, so as to control the cancellation sound field to minimize the sum of the mean square of the error signals.
Preferably, the method for adjusting the weight values of the adaptive filter in step S3 includes:
iteratively calculating the weight value of the adaptive filter, wherein the expression is as follows:
W a (j+1)＝W a (j)+NS(j-n-K)ε(j) (9)
w in formula (9) a (j) The weight of the nth branch of the adaptive filter at the sampling moment j;
s (j) input signal sample values;
e (j) error signal sample values;
n is a parameter for controlling stability and convergence rate;
k-acoustic delay, K must be equal to the feedback delay of out (t) to e (t) over the entire frequency band of interest.
In acoustic noise cancellation, unlike pure electrical noise cancellation, the transfer functions of the acousto-electric, electro-acoustic sensors and the transfer function of the spatial propagation of acoustic noise, in particular the acoustic delay, must be considered. Since the sound waves emitted by the secondary source are not immediately received by the error sensor, but rather undergo an acoustic delay of K, the effect of this acoustic delay on the system is crucial.
Specifically, the method for driving the cancellation loudspeaker to generate the cancellation sound field in the space in step S3 includes that the cancellation electric signal is connected to the cancellation loudspeaker after D/a conversion and analog power amplification, and the cancellation sound wave generated by the cancellation loudspeaker is superimposed with the magnetic resonance sound noise in the local space near the acoustic noise sensor array, so as to weaken the influence of the magnetic resonance sound signal.
The method for controlling the cancellation field to minimize the mean square sum of error signals comprises the following steps: the RVSS-FXLMS structure is adopted, based on the filtering type minimum mean square FXLMS, and the variable convergence factor RVSS algorithm is matched to increase the convergence speed and stability of the FXLMS according to the control state.
The construction method of the RVSS-FXLMS framework comprises the following steps: in an active acoustic noise control mode, a self-adaptive filter generates a feedforward reverse electric signal to inhibit a focusing error caused by a low-frequency signal.
The embodiment of the invention also provides a system for canceling the sound and noise by using the magnetic resonance sound wave, which executes the method for canceling the sound and noise by using the magnetic resonance sound wave, and comprises the following steps:
a sampling and quantization module: an array of acoustic noise sensors is deployed at the source of the magnetic resonance acoustic noise, by which the initial magnetic resonance acoustic noise analog signal is sampled and quantized, creating a primary channel.
A characteristic signal extraction module: for extracting x [n] The characteristic signal of the magnetic resonance acoustic noise comprises amplitude and phase; for x [n] Pre-emphasis is performed on the high-frequency part of the magnetic resonance acoustic noise; for x [n] Windowing the characteristic signal of the magnetic resonance acoustic noise; extracting discrete frequency band spectrum information from each windowed frame characteristic signal; corresponding the frequency output by DFT to mel scale, and calculating the logarithmic energy output by each mel filter bank after acquiring mel frequency spectrum; the mel frequency spectrum is converted back to a time domain signal, and a cepstrum coefficient is obtained by discrete cosine transform by utilizing the logarithmic energy of a mel filter; for x [n] Performing energy and difference on the characteristic signals of the magnetic resonance acoustic noise;
the active cancellation and noise reduction module: the device is used for converting the characteristic signal into an electric signal as an input reference signal of the adaptive filter, performing adaptive filtering processing on the input reference signal, outputting a cancellation electric signal to establish a secondary sound channel, and driving a cancellation loudspeaker to generate a cancellation sound field in space; and the secondary sound channel acquires residual signals in real time to perform feedback adjustment, and reduces the magnetic resonance acoustic noise to be below a preset threshold value after multiple iterations.
The active cancellation and noise reduction module comprises:
a secondary channel sub-module: the cancellation signal is accessed to the cancellation loudspeaker after D/A conversion and analog power amplification, and cancellation sound waves generated by the cancellation loudspeaker are superposed with magnetic resonance sound noise in a local space near the sound noise sensor array, so that the influence of the magnetic resonance sound signals is weakened;
an error signal feedback sub-module: the method is used for monitoring the acoustic noise cancellation condition and feeding an error signal back to the adaptive filter to adjust the weight of the adaptive filter, so that the cancellation field is controlled to minimize the mean square sum of the error signal.
According to the invention, the secondary sound field is generated in the space, so that the primary sound field and the secondary sound field are superposed to reduce the sound pressure of the area so as to eliminate the sound noise, and a corresponding sound signal extraction method and a self-adaptive filtering algorithm are designed, so that the efficiency of reducing the sound noise is greatly improved, and the sensitivity of personnel is improved.
Fig. 2 is a schematic structural diagram of a computer device provided in an embodiment of the present invention; referring to fig. 2 of the drawings, the computer apparatus comprises: an input device 23, an output device 24, a memory 22 and a processor 21; the memory 22 for storing one or more programs; when the one or more programs are executed by the one or more processors 21, the one or more processors 21 are enabled to implement the method for canceling acoustic noise by magnetic resonance acoustic waves as provided in the above embodiments; wherein the input device 23, the output device 24, the memory 22 and the processor 21 may be connected by a bus or other means, as exemplified by the bus connection in fig. 2.
The memory 22, which is a storage medium readable and writable by a computing device, may be used to store a software program, a computer executable program, and program instructions corresponding to the method for canceling acoustic noise by using magnetic resonance acoustic waves according to the embodiment of the present invention; the memory 22 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the device, and the like; further, the memory 22 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device; in some examples, the memory 22 may further include memory located remotely from the processor 21, which may be connected to the device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 23 may be used to receive input numeric or character information and to generate key signal inputs relating to user settings and function control of the apparatus; the output device 24 may include a display device such as a display screen.
The processor 21 executes software programs, instructions and modules stored in the memory 22 to execute various functional applications and data processing of the apparatus, that is, to implement the above-mentioned method for canceling acoustic noise by using magnetic resonance acoustic waves.
The computer equipment provided by the above can be used for executing the method for canceling the acoustic noise by using the magnetic resonance sound wave provided by the above embodiment, and has corresponding functions and beneficial effects.
Embodiments of the present invention also provide a storage medium containing computer executable instructions, which when executed by a computer processor, are used to perform the method for canceling acoustic noise by magnetic resonance acoustic waves as provided in the above embodiments, the storage medium being any of various types of memory devices or storage devices, the storage medium comprising: mounting media such as CD-ROM, floppy disk, or tape devices; computer system memory or random access memory such as DRAM, DDR RAM, SRAM, EDO RAM, Lanbas (Rambus) RAM, etc.; non-volatile memory such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc.; the storage medium may also include other types of memory or combinations thereof; in addition, the storage medium may be located in a first computer system in which the program is executed, or may be located in a different second computer system connected to the first computer system through a network (such as the internet); the second computer system may provide program instructions to the first computer for execution. A storage medium includes two or more storage media that may reside in different locations, such as in different computer systems connected by a network. The storage medium may store program instructions (e.g., embodied as a computer program) that are executable by one or more processors.
Of course, the storage medium containing the computer-executable instructions provided by the embodiments of the present invention is not limited to the method for canceling acoustic noise by using magnetic resonance acoustic waves according to the above embodiments, and may also perform the relevant operations in the method for canceling acoustic noise by using magnetic resonance acoustic waves according to any embodiments of the present invention.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention; various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for canceling acoustic noise by using magnetic resonance sound waves is characterized by comprising the following steps:
s1, deploying an array of acoustic noise sensors at the magnetic resonance acoustic noise source, and sampling and quantizing the initial magnetic resonance acoustic noise analog signals through the acoustic noise sensors to establish a primary sound channel;
the sampled and quantized waveform is denoted x [n] Where n is a time index; storing the sampled samples as amplitude values;
s2, extracting x [n] The characteristic signal of the magnetic resonance acoustic noise comprises amplitude and phase;
the extraction x [n] The method for the characteristic signal of the magnetic resonance acoustic noise comprises the following steps:
A. for x [n] Pre-emphasis is performed on the high-frequency part of the magnetic resonance acoustic noise;
the pre-emphasis method is to pre-emphasize x by a high-pass filter [n] High frequency energy of (a), high pass filter denoted as y [n] ＝x [n] -ax [n-1] A is a filter coefficient;
B. for x [n] Windowing the characteristic signal of the magnetic resonance acoustic noise;
from the characteristic signal s [n] The process of extracting one frame is denoted as y [n] ＝w [n] s [n] In the MFCC algorithm, windowingUsing a hamming window with edge smoothing down to 0, the expression is as follows:
in the formula (1), L is the frame length;
C. extracting discrete frequency band spectrum information from each windowed frame characteristic signal;
the method for extracting the frequency spectrum information of the discrete frequency band adopts Discrete Fourier Transform (DFT), and the expression is as follows:
in the formula (2), the input of DFT is x [n] The output of each windowed signal is a complex number X comprising N frequency bands [k] ，X [k] Representing the original signal x [n] The amplitude and phase of a certain frequency component;
calculating expression (2) of DFT by fast fourier transform FFT, N being a power of 2;
D. the frequency output by DFT is corresponding to mel scale; the correspondence between the frequency and the mel scale is linear below 1000Hz and logarithmic above 1000Hz, and the calculation formula of the frequency output by DFT corresponding to the mel scale is as follows:
in formula (3), f is frequency in Hz;
mixing X [k] The FFT spectrum is converted into mel spectrum by a mel filter bank with M filters, the adopted mel filter is a triangular filter, and the center frequency is f (m) And M is 1, 2, …, M, and the frequency of each triangular filter is expressed as:
in the formula (4), the reaction mixture is,
after the mel frequency spectrum is obtained, calculating the logarithmic energy output by each mel filter bank; the logarithmic energy output by each mel-filter is:
E. the mel frequency spectrum is converted back to a time domain signal, and by utilizing the logarithmic energy of the mel filter, cepstrum coefficients are obtained by discrete cosine transform:
in the formula (6), L refers to MFCC order, and M refers to the number of triangular filters;
F. for x [n] Performing energy and difference on the characteristic signals of the magnetic resonance acoustic noise; the energy of a frame is defined as the sum of the squares of the sample points of a frame, and for a windowed signal x, the energy from sample point t1 to sample point t2 is:
adding features to represent the dynamic change of the frames, and calculating the difference value of 13 features of each frame before and after the current frame:
in the formula (8), c (t +1) is a frame next to the current frame, and c (t-1) is a frame before the current frame;
s3, converting the characteristic signal into an electric signal as an input reference signal of a self-adaptive filter, carrying out self-adaptive filtering processing on the input reference signal, outputting a cancellation electric signal to establish a secondary sound channel, and driving a cancellation loudspeaker to generate a cancellation sound field in space; and the secondary sound channel acquires residual signals in real time to perform feedback adjustment, and reduces the magnetic resonance acoustic noise to be below a preset threshold value after multiple iterations.
2. The method for canceling acoustic noise according to claim 1, wherein the adaptive filtering processing in S3 includes deploying an array of error sensors at a location of the magnetic resonance apparatus where the acoustic noise needs to be reduced, monitoring the cancellation condition of the acoustic noise, and feeding an error signal back to the adaptive filter to adjust the weight of the adaptive filter, so as to control the cancellation sound field to minimize the sum of the mean square of the error signal.
3. The method for canceling acoustic noise by using magnetic resonance sound waves according to claim 2, wherein the method for adjusting the weight of the adaptive filter in the step S3 includes:
iteratively calculating the weight value of the adaptive filter, wherein the expression is as follows:
W a (j+1)＝W a (j)+NS(j-n-K)ε(j) (9)
w in formula (9) a (j) The weight of the nth branch of the adaptive filter at the sampling moment j is calculated;
s (j) input signal sample values;
epsilon (j) is the error signal sampling value;
n is a parameter for controlling stability and convergence rate;
k is the acoustic delay.
4. The method for canceling and reducing acoustic noise by using magnetic resonance sound waves according to claim 1, wherein the step S3 includes the step of connecting the cancellation electric signal to the cancellation loudspeaker after D/a conversion and analog power amplification, and the cancellation sound waves generated by the cancellation loudspeaker are superimposed with the magnetic resonance sound noise in the local space near the acoustic noise sensor array, so as to reduce the influence of the magnetic resonance sound signal.
5. The method for canceling acoustic noise by using magnetic resonance sound waves according to claim 2, wherein the method for controlling the cancellation field to minimize the sum of mean square of error signals comprises: the RVSS-FXLMS structure is adopted, based on the filtering type minimum mean square FXLMS, and the variable convergence factor RVSS algorithm is matched to increase the convergence speed and stability of the FXLMS according to the control state.
6. The method for canceling acoustic noise by using magnetic resonance acoustic waves according to claim 5, wherein the construction method of the RVSS-FXLMS architecture comprises the following steps: in an active acoustic noise control mode, a feedforward reverse electric signal is generated by an adaptive filter to inhibit a focusing error caused by a low-frequency signal.
7. A magnetic resonance acoustic wave cancellation acoustic noise system, characterized in that the magnetic resonance acoustic wave cancellation acoustic noise method according to any one of claims 1 to 6 is performed, and comprises:
a sampling and quantization module: an array of acoustic noise sensors is deployed at the source of the magnetic resonance acoustic noise, by which the initial magnetic resonance acoustic noise analog signal is sampled and quantized, creating a primary channel.
A feature signal extraction module: for extracting x [n] The characteristic signal of the magnetic resonance acoustic noise comprises amplitude and phase; for x [n] Pre-emphasis is performed on the high-frequency part of the magnetic resonance acoustic noise; for x [n] Windowing the characteristic signal of the magnetic resonance acoustic noise; extracting discrete frequency band spectrum information from each windowed frame characteristic signal; corresponding the frequency output by DFT to mel scale, and calculating the logarithmic energy output by each mel filter bank after acquiring mel frequency spectrum; the mel frequency spectrum is converted back to a time domain signal, and a cepstrum coefficient is obtained by discrete cosine transform by utilizing the logarithmic energy of a mel filter; for x [n] Performing energy and difference on the characteristic signals of the magnetic resonance acoustic noise;
the active cancellation and noise reduction module: the device is used for converting the characteristic signal into an electric signal as an input reference signal of the adaptive filter, performing adaptive filtering processing on the input reference signal, outputting a cancellation electric signal to establish a secondary sound channel, and driving a cancellation loudspeaker to generate a cancellation sound field in space; and the secondary sound channel acquires residual signals in real time to perform feedback adjustment, and reduces the magnetic resonance acoustic noise to be below a preset threshold value after multiple iterations.
8. The magnetic resonance acoustic wave-to-acoustic-noise cancellation system according to claim 7, wherein the active cancellation acoustic noise cancellation module comprises:
a secondary channel sub-module: the cancellation signal is accessed to the cancellation loudspeaker after D/A conversion and analog power amplification, and cancellation sound waves generated by the cancellation loudspeaker are superposed with magnetic resonance sound noise in a local space near the sound noise sensor array, so that the influence of the magnetic resonance sound signals is weakened;
an error signal feedback sub-module: the method is used for monitoring the acoustic noise cancellation condition and feeding an error signal back to the adaptive filter to adjust the weight of the adaptive filter, so that the cancellation field is controlled to minimize the mean square sum of the error signal.
9. A computer readable storage medium, having a computer program stored thereon, wherein the program, when executed by a processor, performs the steps of the method for canceling acoustic noise by magnetic resonance acoustic waves according to any one of claims 1 to 8.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the method for canceling acoustic noise by magnetic resonance acoustic waves according to any one of claims 1 to 8.
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