CN115361617A - Non-blind area multi-microphone environmental noise suppression method - Google Patents
Non-blind area multi-microphone environmental noise suppression method Download PDFInfo
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Abstract
The invention discloses a blind-area-free multi-microphone environmental noise suppression method, which is used for the environmental noise suppression of a telephone traffic type earphone provided with a main microphone and N auxiliary microphones, wherein N is an even number which is more than or equal to 2, each earphone side is provided with at least one auxiliary microphone, and each earphone side obtains an auxiliary microphone synthesis signal through the synthesis of at least one auxiliary microphone; calculating the noise power spectrum of the synthesized signal of the auxiliary microphone, and calculating the noise power spectrum of the main microphone through mapping of a frequency domain; and removing the noise signal of the main microphone by using a noise reduction algorithm based on the noise power spectrum and the power spectrum of the signal with noise according to the noise power spectrum and the power spectrum of the signal with noise of the main microphone, and converting the obtained frequency spectrum signal with noise reduction into a time domain signal for output. The invention can improve the integral noise reduction effect of the environmental noise.
Description
Technical Field
The invention relates to the technical field of audio processing, in particular to a non-blind-area multi-microphone environmental noise suppression method.
Background
The headset with the call may be exposed to noise from all directions and the person wearing the traffic headset, as shown in fig. 1, may have noise sources from either the left or the right. The communication earphone with the ambient noise reduction function in the market has two modes of single-microphone and double-microphone noise reduction, generally, the main microphone 20 is arranged in a microphone rod near the mouth of a person and used for collecting the voice of the person speaking, and the auxiliary microphone 10 is arranged on the outer side of one of the ear bags and used for collecting the ambient noise.
The main microphone in the conventional dual-microphone noise reduction scheme adopts a directional microphone to collect the sound emitted by the mouth of a person as much as possible and reduce the collected noise amount to improve the signal-to-noise ratio of the main microphone, and the auxiliary microphone is usually an omnidirectional microphone to collect the noise from all directions. The dual-microphone noise reduction algorithm estimates the environmental noise in the main microphone by using the environmental noise collected by the auxiliary microphone, so that the noise reduction of the dual microphone is realized by using the noise reduction algorithm (such as wiener filtering, statistical algorithm based on minimum mean square error, neural network model based on signal-to-noise ratio and the like) of the conventional single microphone.
The auxiliary microphone is used for estimating the power spectrum of the noise in the main microphone, namely the power spectrum of the main microphone is estimated through the power spectrum of the auxiliary microphone, the noise source is far, and the power spectrum of the noise reaching the main auxiliary microphone is basically the same, so that the power spectrum of the main microphone can be estimated through simple spectrum mapping by only calculating the frequency response of the auxiliary microphone to the main microphone.
The noise in real life may be steady-state air conditioning sound, and may also be transient noise such as keyboard tapping, so it is not good to estimate the noise power spectrum of the auxiliary microphone by using the steady-state noise method, and usually the power spectrum of the auxiliary microphone is taken directly as the estimation of the noise power spectrum of the current auxiliary microphone.
In practical tests, if the noise source comes from one side of the auxiliary microphone, the double-microphone noise reduction algorithm has a good effect, and the closer the noise source is to the auxiliary microphone, the better the performance of the whole noise reduction algorithm is. However, if the noise source is from the other side of the auxiliary microphone, the noise source can not directly reach the auxiliary microphone but can directly reach the main microphone, so that the signal-to-noise ratio of the main microphone is reduced, the signal-to-noise ratio of the auxiliary microphone is reduced, and the performance of the whole noise reduction algorithm is greatly reduced.
As described above, in traffic and examination applications, noise sources may come from various directions, and the conventional main microphone + strong side auxiliary microphone can improve the overall noise reduction effect of the noise sources from one side of the auxiliary microphone, and often the noise reduction effect of the noise sources from the other side of the auxiliary microphone is not good.
Disclosure of Invention
The invention aims to solve the problems in the prior art and provides a non-blind-zone multi-microphone environmental noise suppression method, which is characterized in that the number of auxiliary microphones of a telephone traffic type earphone is increased, each side forms a synthesized auxiliary microphone signal through one or more auxiliary microphones, the voice emitted by the mouth of a person in the auxiliary microphone is suppressed, the noise signal collected by the auxiliary microphone is improved as much as possible, the auxiliary microphone noise is improved, the estimated auxiliary microphone noise is more accurate, and the whole environmental noise reduction effect is improved.
The technical scheme adopted for realizing the purpose of the invention is as follows:
a blind-zone-free multi-microphone environmental noise suppression method is used for environmental noise suppression of a telephone traffic type earphone configured with a main microphone and N auxiliary microphones, wherein N is an even number which is more than or equal to 2, each earphone side is configured with at least one auxiliary microphone, and each earphone side obtains an auxiliary microphone synthesis signal through synthesis of at least one auxiliary microphone;
calculating the noise power spectrum of the synthesized signal of the auxiliary microphone, and calculating the noise power spectrum of the main microphone through mapping of a frequency domain;
and removing the noise signal of the main microphone by using a noise reduction algorithm based on the noise power spectrum and the power spectrum of the signal with noise according to the noise power spectrum and the power spectrum of the signal with noise of the main microphone, and converting the obtained frequency spectrum signal with noise reduction into a time domain signal for output.
Preferably, the auxiliary microphone synthesized signal is formed by a beamforming method or formed by a smoothing synthesis method.
Preferably, when the signal is formed by the smooth synthesis method, the mean square error of the amplitude of the auxiliary microphone signal is calculated, then the respective mixing ratio is determined and calculated according to the respective mean square error of the amplitude, and the signal is formed by inverse mixing.
Preferably, when the signal is formed by the beamforming method, the signal collected by the two auxiliary microphones is filtered by using a beamforming filter matched with the corresponding second-order cone plan according to the positioning result of the noise source of the auxiliary microphones, and a final synthesized signal of the auxiliary microphones is output.
Preferably, the auxiliary microphone is updated with the noise power spectrum of the auxiliary microphone synthesized signal using the frequency division point speech existence probability of the main microphone, the noise power spectrum of the auxiliary microphone synthesized signal is adjusted, and the lower limit value of the auxiliary microphone synthesized signal noise power spectrum is set and the lower limit value of the noise power spectrum of the main microphone is estimated based on the lower limit value.
Wherein the adjusting the auxiliary noise power spectrum comprises:
noise power spectrum Psd of the average signal of the secondary microphone n5 Multiplying (n, k) by the gain factor g (n, k) is regarded as the lower limit of the noise power spectrum of the synthesized auxiliary microphone synthesized signal, and the noise power spectrum of the auxiliary microphone synthesized signal adjusted by the lower limit is obtained
Is represented as;
g(n，k)＝1.0-0.5*SPP m1 (n，k)
in the above formula, SPP m1 (n, k) is the dividing point speech existence probability of the main microphone, psd m4 (n, k) is the power spectrum of the noise signal of the synthesized signal of the auxiliary microphone, psd n5 (n, k) is the noise power spectrum of the auxiliary microphone average signal.
Wherein said computing of the noise power spectrum of the auxiliary microphone combined signal and through the frequency domainAfter the noise power spectrum of the main microphone is mapped and calculated, the noise power spectrum of the main microphone is multiplied by a gain factor g (n, k) to be used as the final noise power spectrum Psd of the main microphone n1-final (n, k) to obtain a noise power spectrum Psd of the primary microphone adjusted by the lower limit n1-final (n，k)：
Psd n1-final (n，k)＝max(Psd n4-map (n，k)，Psd n1 (n，k)*g(n，k))
Wherein Psd n4-map (n, k) is the noise power spectrum of the combined signal of the secondary microphone mapped to the noise power spectrum of the primary microphone, psd n1 (n, k) is the noise power spectrum of the primary microphone, psd m1 (n, k) is the noisy signal power spectrum estimate of the main microphone, psd m4 (n, k) is a noisy signal power spectrum estimate of the synthesized signal of the auxiliary microphone, β is a parameter between 0 and 1 representing a smoothing factor of the mapping gain factor, η (n, k) being performed only if the primary microphone speech is detected as being speech-free.
According to the method for suppressing the environmental noise of the multi-microphone without the blind area, the synthesized auxiliary microphone signal is formed by the auxiliary microphones of the telephone traffic type earphone, the sound emitted by the mouth of a person in the auxiliary microphone is suppressed, the noise signal collected by Gao Fumai is provided, so that the noise-signal ratio of the auxiliary microphone is improved, the estimated auxiliary microphone noise is more accurate, and the integral environmental noise reduction effect is improved.
Drawings
Fig. 1 is a schematic diagram of a microphone arrangement of a dual-microphone noise reduction microphone in the prior art.
Fig. 2 is a schematic diagram of an auxiliary microphone arrangement for the blind-zone-free multi-microphone ambient noise suppression system of the present invention.
Fig. 3 is an ambient noise suppression flow diagram of the non-blind zone multi-microphone ambient noise suppression system of the present invention.
Fig. 4 is a schematic diagram of a beamforming algorithm for multi-secondary synthesis.
Detailed Description
The invention is described in further detail below with reference to the figures and specific examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
In the present application, primary microphone is synonymous with primary microphone, and secondary microphone, secondary microphone and secondary microphone are synonymous.
For a telephone headset with a primary microphone and a secondary microphone, assuming that the signal emitted by the mouth is s (t), the ambient noise is n (t), the transfer function from the mouth to the primary microphone is h1 (t), and the transfer function from the mouth to the secondary microphone is h2 (t), the primary microphone and the secondary microphone signals are expressed as:
And transforming the time domain signals of the main microphone and the auxiliary microphone into a frequency domain through short-time Fourier transform to obtain:
M1(n，k)＝H1(n，k)*S(n，k)+N1(n，k)＝S1(n，k)+N1(n，k) (3)
M2(n，k)＝H2(n，k)*S(n，k)+N2(n，k)＝S2(n，k)+N2(n，k) (4)
wherein, M1 (N, k), H1 (N, k), S (N, k), N1 (N, k), S1 (N, k), M2 (N, k), H2 (N, k), N2 (N, k), S2 (N, k) are short-time fourier transform frequency domain expressions of time domain signals M1 (t), H1 (t), S (t), N1 (t), S1 (t), M2 (t), H2 (t), N2 (t), S2 (t), respectively, and x represents a multiplication operation.
Calculating the PSD of the main microphone and the auxiliary microphone with noise signals according to the short-time Fourier transform result;
Psd m1 (n，k)＝||M1(n，k)|| 2 ＝||S1(n，k)+N1(n，k)|| 2 (5)
Psd m2 (n，k)＝||M2(n，k)|| 2 ＝||S2(n，k)+N2(n，k)|| 2 (6)
the auxiliary microphone is used for estimating the power spectrum of noise in the main microphone, namely the power spectrum of the environmental noise N1 (N, k) of the main microphone is estimated through the power spectrum of the environmental noise N2 (N, k) of the auxiliary microphone, the noise source is usually far, and the power spectrum of the noise reaching the main auxiliary microphone is basically the same, so that the power spectrum of the environmental noise N1 (N, k) can be estimated through the power spectrum of the environmental noise N2 (N, k) of the auxiliary microphone through simple spectrum mapping as long as the frequency response from the auxiliary microphone to the main microphone is calculated.
For the ambient noise signal n (t), the noise in real life may be steady-state air conditioning sound, and may also be transient noise such as keyboard tapping, so it is not good to estimate the noise power spectrum of the auxiliary microphone by using the steady-state noise method, and usually the power spectrum of the noisy signal of the auxiliary microphone is taken directly as the estimation of the noise power spectrum of the current auxiliary microphone, that is:
thus, the noise-to-signal ratio in the auxiliary microphone, i.e.
Will become the key to the overall noise reduction algorithm.
Meanwhile, from actual test, if the noise source comes from one side of the auxiliary microphone, the double-microphone noise reduction algorithm has a good effect, and the closer the noise source is to the auxiliary microphone, the better the performance of the whole noise reduction algorithm is. However, if the noise source is from the other side of the auxiliary microphone, the noise source can not directly reach the auxiliary microphone but can directly reach the main microphone, so that the signal-to-noise ratio of the main microphone is reduced, the signal-to-noise ratio of the auxiliary microphone is reduced, and the performance of the whole noise reduction algorithm is greatly reduced.
Therefore, in the method for suppressing environmental noise of multiple microphones without blind areas according to the embodiments of the present invention, an auxiliary microphone is added at the auxiliary microphone side, and a pair of symmetrical auxiliary microphones is configured, as shown in fig. 2, signals are synthesized by two auxiliary microphones to overcome the problem of estimating the noise at the strong side and the weak side of the auxiliary microphone, and no matter from which direction the noise comes, the auxiliary microphone signals synthesized by two auxiliary microphones will effectively reduce the frequency spectrum of the speech signal emitted from the mouth of the user in the synthesized signal without reducing the noise power spectrum in the synthesized signal, thereby improving the noise-to-signal ratio of the synthesized auxiliary microphone signals.
Of course the invention can naturally also be extended to scenarios with more than 2 auxiliary microphones.
According to the embodiment of the invention, the auxiliary microphone signal for estimating the noise power spectrum of the main microphone is obtained by synthesizing the two auxiliary microphone signals, so that the auxiliary microphones from the left and right earmuffs enable the noise to be collected without blind areas. The leakage of clean speech from the direction of the human mouth to the auxiliary microphones can be suppressed by signal processing methods such as beam forming by means of the two auxiliary microphones.
The signal synthesis of the whole auxiliary microphone can be realized by a software algorithm, and can also be realized by simple hardware circuit filtering and sound mixing, and in the hardware realization, except for redundant auxiliary microphones, the complexity of additional auxiliary circuits is low, and the realization is easy.
It should be noted that, in the embodiment of the present invention, after the noise power spectrum of the auxiliary microphone combined signal is obtained, the noise power spectrum of the main microphone may be synthesized through mapping of the frequency domain, so as to obtain an accurate noise power spectrum of the main microphone. By obtaining the accurate noise power spectrum of the main microphone, various algorithms based on the signal-to-noise ratio can well remove the noise signal of the main microphone.
The obtained main microphone noise power spectrum can be used for a conventional single microphone noise reduction algorithm, such as a spectral subtraction method, a wiener filtering method, various spectral domain statistical algorithms based on minimum mean square error, and the like, and a latest single microphone noise reduction algorithm based on a neural network, so as to perform noise reduction processing on a main microphone, which is the prior art and is not described again.
The noise power spectrum of the auxiliary microphone is obtained, and the noise power spectrum of the main microphone can be synthesized by mapping the frequency domain, and the mapping factor of the mapping of the frequency spectrum can be obtained by smoothing the power spectrum of the main microphone and the power spectrum mapping factor of the auxiliary microphone when the voice is inactive, as shown in formula (15), which will be described in detail later.
The accurate noise power spectrum of the main microphone also provides good signal-to-noise ratio reference for voice activity detection of the main microphone, and a simple voice detection algorithm based on the signal-to-noise ratio can be designed, so that the implementation complexity of the whole algorithm is reduced. Since the conventional double-microphone noise reduction algorithm and the single-microphone noise reduction algorithm are related in many places, the noise reduction algorithm is not described in detail.
In the following section of the present invention, it will be emphasized how to implement noise power spectrum synthesis of multiple auxiliary microphones, and without loss of generality, the implementation of installing one auxiliary microphone on each earphone side is taken as an example.
Because a plurality of auxiliary microphones are installed, and the microphone has different standards when leaving factory, the sensitivity error of the microphone is usually +/-2 dB, and the sensitivity error can be improved to +/-1 dB through sorting, but even then, the microphone installed on the auxiliary microphone still has the sensitivity error, so that when leaving factory, the sensitivity calibration can be carried out through a signal right in front of the earphone, and the calibration value can be contained in a factory detection program and directly updated to a factory version firmware parameter.
Let the signal of the second auxiliary signal acquisition on the other earphone side be expressed as:
in the formula, n3 (t) is an environmental noise signal acquired by the second auxiliary microphone signal, s3 (t) is a signal sent by the mouth acquired by the second auxiliary microphone, s (t) is a signal sent by the mouth, and h3 (t) is a transmission function from the mouth to the second auxiliary microphone.
After the microphone calibration, the direction of the noise source can be roughly judged according to the amplitude of the auxiliary microphone signal, so that the mixing ratio of the two auxiliary microphones can be calculated according to the mean square error of the amplitudes of the microphone signals. Furthermore, the synthesized signal of the auxiliary microphone can be synthesized by a smooth synthesis algorithm, which is calculated as follows:
where α is a parameter between 0 and 1, representing a smoothing factor of a statistical time slot of a magnitude mean square Rms (Root mean square), N is a window length for calculating the magnitude mean square Rms, i is a frame number for framing with a window, rms2 (b), rms3 (b) are the magnitude mean square of the signal acquired by the auxiliary microphone of each earphone side, respectively.
The auxiliary microphone signal synthesized by the two auxiliary microphones is:
in the formula, m2 (t) and m3 (t) are respectively noisy signals collected by auxiliary microphones on the left side and the right side of an earphone;
in general, the signals leaked by the human mouth to the two auxiliary microphones are basically consistent, i.e. s2 (t) and s3 (t) are highly correlated, and the corresponding noise signals n2 (t) and n3 (t) are relatively independent, so that an inverse phase is made during the upmixing.
Illustratively, the synthesized signal of the auxiliary microphone may be formed by a beam forming algorithm, in addition to the smooth synthesis algorithm, as described above, as shown in fig. 4. According to the noise source positioning results of the two auxiliary microphones (if no suitable noise source position exists, the 0-degree direction is used), under the condition that the beam suppression is carried out in the 90-degree human mouth direction, the beam forming of the noise source position is carried out, and the beam forming of the multi-target constraint can be realized by using the conventional second-order cone programming.
In the embodiment of the invention, the sound source positioning precision does not need to be very high, for example, about 10 degrees, a beam forming filter with a required angle is designed by using second-order cone planning in advance, and a proper filter can be selected according to the position of a noise source when auxiliary microphone signals are synthesized; the final synthesized integrated auxiliary signal is:
wherein f3 (t) and f2 (t) are beam forming filters of a pre-stored second order cone plan selected according to the noise source angle.
Because the voice signal leaks to the auxiliary microphone when the human mouth speaks, in order to improve the accuracy of estimating the noise power spectrum, when the synthesized signal of the auxiliary microphone is formed by a smooth synthesis method, the inverse sound mixing is adopted, so as to further improve the accuracy of the noise power spectrum of the synthesized signal. Similarly, when the synthesized signal of the auxiliary microphone is formed by the beam forming method, and when the beam forming is realized, the signal for restraining the 90-degree human mouth direction is selected to a certain range as a second-order cone target when the filter is designed, so that the leakage of the voice signal to the auxiliary microphone can be effectively reduced, and the useful signal component in the auxiliary microphone can be reduced.
For the auxiliary microphones of the telephone traffic type earphone, no matter the beam forming algorithm or the smooth synthesis algorithm, the synthesized noise power spectrum obtained by the beam forming algorithm or the smooth synthesis algorithm approaches to the result of the inverse mixing of the two auxiliary microphones, if the noise is a low-frequency component and the wavelength is longer, the noise signal obtained by the inverse mixing is greatly reduced, and therefore, the problem of noise power spectrum reduction caused by the synthesis of multiple auxiliary microphones needs to be solved.
In order to solve the problem of noise power spectrum reduction caused by multi-auxiliary wheat synthesis, the invention provides the following technology:
and updating the noise power spectrum averagely output by the algorithm of the whole auxiliary microphone by using the voice existence probability SPP of the main microphone, setting a lower limit value of the noise power spectrum of the synthesized signal of the auxiliary microphone, and estimating the noise power spectrum of the main microphone by taking the lower limit value as the noise power spectrum when the noise power spectrum of the synthesized signal of the auxiliary microphone is lower than the lower limit value.
Setting the average auxiliary signal as:
the Speech existence Probability (SPP) of the frequency dividing point of the main wheat is calculated by a formula (14) according to the noise estimation power spectrum and the current frequency point power spectrum in the main wheat.
And ξ is a preset SNR (signal to noise ratio) value (such as 15 dB) marked as voice, and π is 180 degrees corresponding radian.
Estimated noise power spectrum Psd of auxiliary wheat averaged signal n5 (n, k) and noise power spectrum Psd of the main microphone n1 The (n, k) can be obtained by a conventional noise power spectrum algorithm based on SPP, that is, after the dividing point speech existence probability SPP of the main microphone of the microphone is estimated, a noise power spectrum is obtained by using bayesian estimation based on SPP, and related algorithms are numerous and will not be described herein. Of course other single microphone noise power spectrum algorithms are possible.
Wherein the estimated noise power spectrum Psd of the auxiliary wheat averaged signal n5 Multiplying (n, k) by the gain factor g (n, k) is taken as the lower limit of the noise power spectrum of the synthesized auxiliary signal, i.e. the noise power spectrum of the auxiliary signal adjusted by the lower limit
Expressed as:
g(n，k)＝1.0-0.5*SPP m1 (n，k)
similarly, the synthesized signal noise power spectrum at the secondary microphone is mapped to the primary microphone noise power spectrum (Psd) n4-map (n, k)) and finally the noise power spectrum Psd in the primary microphone n1 The (n, k) multiplied by the gain factor g (n, k) can also be used as the final noise power spectrum Psd of the main microphone n1-final The lower limit of (n, k) is shown in equation (15):
where β is a parameter between 0 and 1 representing the smoothing coefficient of the mapping gain factor, it is noted that the mapping gain factor η (n, k) is only performed if the main microphone speech is detected as being without speech.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (7)
1. A blind-area-free multi-microphone environmental noise suppression method is characterized by being used for environmental noise suppression of a telephone traffic type earphone provided with a main microphone and N auxiliary microphones, wherein N is an even number which is more than or equal to 2, at least one auxiliary microphone is arranged on each earphone side, and an auxiliary microphone synthesis signal is obtained by synthesizing at least one auxiliary microphone arranged on each earphone side;
calculating the noise power spectrum of the synthesized signal of the auxiliary microphone, and calculating the noise power spectrum of the main microphone through mapping of a frequency domain;
and removing the noise signal of the main microphone by using a noise reduction algorithm based on the noise power spectrum and the power spectrum of the signal with noise according to the noise power spectrum and the power spectrum of the signal with noise of the main microphone, and converting the obtained frequency spectrum signal with noise reduction into a time domain signal for outputting.
2. The non-blind area multi-microphone ambient noise suppression method of claim 1, wherein the auxiliary microphone synthesis signal is formed by a beamforming method or by a smoothing synthesis method.
3. The non-blind area multi-microphone ambient noise suppression method according to claim 2, wherein the smoothing synthesis is performed by first calculating the mean-square error of the amplitudes of the auxiliary microphone signals, then determining and calculating the respective mixing ratios according to the respective mean-square errors of the amplitudes, and performing inverse mixing.
4. The method of claim 2, wherein the forming by beamforming is performed by filtering the signals collected by the two auxiliary microphones by using a beamforming filter matched with the corresponding second order cone plan according to the positioning result of the noise source of the auxiliary microphones, and outputting the final synthesized signal of the auxiliary microphones.
5. The non-blind area multi-microphone ambient noise suppression method according to claim 1, wherein the auxiliary microphone updates the noise power spectrum of the auxiliary microphone synthesized signal using the speech existence probability at the division point of the main microphone, adjusts the noise power spectrum of the auxiliary microphone synthesized signal, sets a lower limit value of the noise power spectrum of the auxiliary microphone synthesized signal, and thereby performs the lower limit value of the estimation of the noise power spectrum of the main microphone.
6. The non-blind multi-microphone ambient noise suppression method of claim 5, wherein the adjusting the auxiliary microphone noise power spectrum comprises:
noise power spectrum Psd of the average signal of the auxiliary microphone n5 Multiplying (n, k) by the gain factor g (n, k) is regarded as a lower limit of the noise power spectrum of the synthesized auxiliary microphone composite signal, and the noise power spectrum of the auxiliary microphone composite signal adjusted by the lower limit is obtained
Is represented as;
g(n，k)＝1.0-0.5*SPP m1 (n，k)
in the above formula, SPP m1 (n, k) is the frequency division point speech existence probability of the main microphone, psd m4 (n, k) is the noisy signal power spectrum of the auxiliary microphone combined signal, psd n5 (n, k) is the noise power spectrum of the auxiliary microphone average signal.
7. The non-blind area multi-microphone environmental noise suppression method as claimed in claim 6, wherein the noise power spectrum of the synthesized signal of the auxiliary microphones is calculated, the noise power spectrum of the main microphone is calculated by mapping the frequency domain, and then the noise power spectrum of the main microphone is multiplied by the gain factor g (n, k) to be the final noise power spectrum Psd of the main microphone n1-final (n, k) to obtain a noise power spectrum Psd of the primary microphone adjusted by the lower limit n1-final (n，k)：
Psd n1-final (n，k)＝max(Psd n4-map (n，k)，Psd n1 (n，k)*g(n，k))
Wherein Psd n4-map (n, k) is the noise power spectrum of the combined signal of the secondary microphone mapped to the noise power spectrum of the primary microphone, psd n1 (n, k) is the noise power spectrum of the primary microphone, psd m1 (n, k) is the noisy signal power spectrum of the main microphone, psd m4 (n, k) is the noisy signal power spectrum of the combined signal of the auxiliary microphone, β is a parameter between 0 and 1, representing a smoothing coefficient of the mapping gain factor, which is performed only in case the main microphone speech is detected as no speech.
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