Noise reduction earmuff and method for preserving voice

Abstract

The invention provides a noise reduction earmuff and method capable of retaining voice. The earmuff comprises an earmuff body of a physical sound insulation structure, a pickup microphone, a microprocessor module, an audio drive circuit and a loudspeaker. The pickup microphone collects an environment sound signal; a blind source separation algorithm and a voice signal discrimination algorithm are implanted into the microprocessor module, and the acquired sound signals are processed, separated and screened by using the algorithms to obtain voice signals; the audio drive circuit filters and amplifies a voice signal and transmits the voice signal to the loudspeaker, and the loudspeaker transmits the voice signal to the interior of the earmuff cavity. On the basis of noise isolation of the physical noise reduction earmuff, a blind source separation technology and a voice signal discrimination method are utilized to complete separation and retention of voice signals, and the voice signals are transmitted into the earmuff, so that the voice signals are retained while noise isolation of the earmuff is realized, and a wearer can directly communicate with other people in voice.

Description

Technical Field

[0001] The invention relates to the technical field of blind source separation and noise reduction earphones, in particular to a noise reduction earmuff and a method for retaining speech. Background Art

[0002] Traditional noise reduction earmuffs can isolate most of the noise in daily life. However, these earmuffs isolate voice signals while isolating noise, and the wearer cannot communicate directly with others while wearing the earmuffs.

[0003] Currently, one method of communicating while wearing earmuffs is to use wireless communication. However, this method requires microphones of other devices to transmit sound signals to the earmuffs through wired or wireless means, and must be performed between various devices, without independence. Summary of the invention

[0004] In order to solve the problems of the prior art, the present invention provides a noise reduction earmuff and method for retaining speech. On the basis of physical noise reduction earmuffs isolating noise, blind source separation technology and speech signal discrimination method are used to complete the separation and retention of speech signals and transmit them into the earmuffs, so that the earmuffs can retain speech signals while isolating noise, so that the wearer can have direct speech communication with others.

[0005] The invention provides a noise reduction earmuff for retaining speech, comprising an earmuff with a physical sound insulation structure, wherein a sound pickup microphone sensor for collecting external sound signals is arranged outside the earmuff, a microprocessor module, an audio driving circuit, a loudspeaker and a power supply module are arranged inside the earmuff, the sound pickup microphone sensor is connected to the microprocessor module through an ADC interface, the microprocessor module is connected to the audio driving circuit through a DAC interface, and the audio driving circuit is connected to the loudspeaker through a filter and an amplifier.

[0006] The present invention also provides a method for reducing noise while preserving speech, which is characterized by comprising the following steps:

[0007] 1) Using earmuffs with physical sound insulation structure, and using the external microphone sensor of the earmuffs to collect external sound signals, including noise signals and voice signals;

[0008] 2) Use the convolution blind source separation algorithm to separate the noise signal and the speech signal;

[0009] 3) Determine whether the noise signal and speech signal are completely separated:

[0010] 3.1) Determine the similarity between the two signals based on the cross-correlation method. If the cross-correlation is lower than the set threshold, proceed to the next step. If the cross-correlation is higher than the set threshold, return to step 1);

[0011] 3.2) The correlation degree between a certain signal at different time points is determined based on the autocorrelation discrimination method. If the autocorrelation is lower than the set threshold, the next step is entered; if the autocorrelation is higher than the set threshold, the step returns to step 1);

[0012] 4) Determine whether the separated result is a speech signal or a noise signal based on the short-time energy discrimination method;

[0013] 4.1) Obtain the discriminant formula, assuming that a certain length is L d The signal is x d (n), whose short-time energy is E nd (n), the short-time energy sum is S d , S d The formula is

[0014]

[0015] E nd (n) is normalized to get Its formula is

[0016]

[0017] set up The sum of The formula is

[0018]

[0019] From formulas (4), (5) and (6), we know that

[0020]

[0021] set up The sum of the squares is Q d , whose formula is

[0022]

[0023] 4.2) Assume that the two signals obtained after separation in step 2) are x(n) and y(n), respectively. Cut a length L from the two signals. d The band is substituted into the formula In the above equation, we get Q x and Q y ,like

[0024] Q x >Q y #(9)

[0025] Then it is judged that x(n) is a speech signal and y(n) is a noise signal;

[0026] 5) The speaker inside the earmuff inputs the voice signal into the interior of the earmuff cavity to complete voice retention.

[0027] The discriminant method based on cross - correlation in step 3.1) is specifically as follows:

[0028] Assume that the two separated signals are x(n) and y(n). The cross - correlation function R xy (n) is calculated by the formula

[0029]

[0030] In the formula, n represents the time offset;

[0031] Take the absolute mean value of the cross - correlation of x(n) and y(n) as h, and set the cross - correlation threshold as H. If h < H is satisfied, it indicates that the noise and voice signals are successfully separated.

[0032] The discriminant method based on auto - correlation in step 3.2) is specifically as follows:

[0033] Assume that one of the separated signals is x(n). The auto - correlation function R xx (n) is calculated by the formula

[0034]

[0035] In the formula, n represents the time offset;

[0036] Take the absolute mean value of the auto - correlation as z, and set the auto - correlation threshold as Z. If z < Z is satisfied, it indicates that the noise and voice signals are successfully separated.

[0037] The beneficial effects of the present invention are as follows:

[0038] 1. Based on the traditional noise - canceling earmuffs, a convolutional blind source separation algorithm based on microphone input and a voice signal discriminant algorithm are introduced to complete the separation and retention of voice signals. While the earmuffs reduce noise, they transmit useful voice signals into the interior of the earmuffs, enabling the wearer to communicate directly while wearing the earmuffs.

[0039] 2. The voice signal passes through the microphone of the earmuff itself and is transmitted into the interior of the earmuff after subsequent processing, without the need to be transmitted through other devices, making it more simple and independent in use.

[0040] 3. The convolutional blind source separation algorithm is used to separate the noise signal and the voice signal, reducing the noise directly introduced into the interior of the earmuff;

[0041] 4. The voice signal discriminant algorithm improves the accuracy of the voice signal transmitted into the interior of the earmuff. Description of the Drawings

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 It is a structural block diagram of a noise reduction earmuff for retaining speech proposed by the present invention;

[0044] Figure 2 It is a principle diagram of a noise-reducing earmuff for retaining speech proposed by the present invention;

[0045] Figure 3 is a composition diagram of the earmuff system proposed by the present invention;

[0046] Figure 4 It is an algorithm flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] The present invention provides a noise reduction earmuff for retaining speech, such as Figure 1 As shown, it includes an earmuff with a physical sound insulation structure, a sound pickup microphone sensor for collecting external sound signals is arranged on the outside of the earmuff, a microprocessor module, an audio driving circuit, a speaker and a power module are arranged inside the earmuff, the sound pickup microphone sensor is connected to the microprocessor module through an ADC interface, the microprocessor module is connected to the audio driving circuit through a DAC interface, and the audio driving circuit is connected to the speaker through a filter and an amplifier.

[0049] Noise cancellation is done by passive noise-isolating earmuffs, which eliminate most of the noise through sound-isolating structures and attracting materials.

[0050] The implementation process of speech retention is as follows: the microphone collects sound signals in the environment, which include noise signals and speech signals. The collected signals are processed using the convolution blind source separation algorithm to separate the environmental noise signal and the speech signal; the speech signal is identified using the speech signal discrimination algorithm, and the speech signal is transmitted into the earmuff cavity through the speaker driven by the audio driving circuit.

[0051] like Figure 2As shown, the passive sound-insulating earmuffs serve as the main structure, including a sound-insulating cavity and sound-absorbing material. A microphone is arranged on the outer side of the earmuff for collecting external sound signals, and the sound signals include two types of signals: noise and speech. Two microphones are respectively connected to the ADC interface, and the ADC interface is connected to the microprocessor module as the input of the microprocessor. The microprocessor module performs algorithm processing on the input signal, outputs a signal through the DAC interface, the DAC interface is connected to the audio drive circuit, and the audio drive circuit is connected to the speaker, and the speaker is arranged in the earmuff cavity.

[0052] As Figure 3 shown, the system includes: Physical structure: Physical sound-insulating earmuffs, ADC and DAC interface modules, power supply module, audio drive circuit, microphone array, speaker. Software algorithm composition: Convolutional blind source separation algorithm and speech signal discrimination algorithm.

[0053] The present invention also provides a noise reduction method for retaining speech, and the complete process is as Figure 4 shown, including the following steps:

[0054] 1) Use earmuffs with a physical sound-insulating structure, and collect external sound signals, including noise signals and speech signals, by using a pickup microphone sensor outside the earmuffs;

[0055] 2) Separate the noise signal and the speech signal by using the convolutional blind source separation algorithm;

[0056] 3) Determine whether the noise signal and the speech signal are completely separated:

[0057] 3.1) Discrimination method based on cross-correlation

[0058] Cross-correlation can measure the similarity between two signals. The cross-correlation function R xy (n of the signals x(n) and y(n) is calculated as

[0059]

[0060] n represents the time offset.

[0061] Assume that the two separated signals are x(n) and y(n). In the case of normal separation, noise and speech can be completely separated, and at this time, the cross-correlation between x(n) and y(n) is small. When the separation effect is not good, noise and speech are not completely separated, x(n) and y(n) contain similar signals, and the cross-correlation between the two is large.

[0062] Therefore, a cross-correlation threshold condition h < H can be set. When this condition is met, it indicates that the noise and speech signals are successfully separated. Among them, h can take the absolute mean value of the cross-correlation between x(n) and y(n), and H is the set threshold.

[0063] Self - correlation - based discrimination method

[0064] Autocorrelation represents the degree of correlation between different time points of a signal. The autocorrelation function \(R\) of the signal \(x(n)\) xx (n) is calculated as

[0065]

[0066] where \(n\) represents the time offset.

[0067] The results of blind source separation include three types: speech signals, noise signals, and mixed signals. Speech signals and noise signals are the results of normal separation, while mixed signals are obtained when the separation effect is not good. Speech signals usually do not have obvious periodicity, so their autocorrelation values are small. In contrast, the autocorrelation values of noise signals generated by repeated mechanical movements are larger than those of speech signals.

[0068] Therefore, a threshold condition of autocorrelation \(z < Z\) can be set. When this condition is met, it indicates that the noise and speech signals are successfully separated, and the signal with the smallest autocorrelation value is likely to be the speech signal. Among them, \(z\) can be taken as the absolute mean of the autocorrelation, and \(Z\) is the set threshold.

[0069] 4) Use the discrimination method based on short - time energy to determine whether the separated result is a speech signal or a noise signal;

[0070] The energy of a speech signal changes with time, and its short - time energy is calculated as the energy of each frame of the speech signal under a certain frame length.

[0071] The formula for the short - time energy of the signal \(x(n)\) is as follows

[0072]

[0073] where \(E(n)\) represents the short - time energy at time \(n\), \(\omega(m)\) is the window function, and \(N\) is the window length. Windowing is usually used to emphasize the sample values at the central moment and reduce the influence of sample values at the window boundaries.

[0074] To make a judgment using short - time energy, the short - time energy of the signal needs to be normalized as follows first.

[0075] Suppose there is a signal \(x\) d (n) with a length of \(L\) d , its short - time energy is \(E\) nd (n), and the sum of short - time energy is \(S\) d , and the formula for \(S\) d is

[0076]

[0077] E nd (n) is normalized to get Its formula is

[0078]

[0079] set up The sum of The formula is

[0080]

[0081] From formulas (4), (5) and (6), we know that

[0082]

[0083] So far, the signal x has been d Normalization of the short-time energy of (n).

[0084] set up The sum of the squares is Q d , whose formula is

[0085]

[0086] From formula (7), we can know of is a constant value. According to mathematical knowledge, if and only if When d Get the minimum value. The closer to the mean, the higher Q d The smaller; on the contrary, Q d The bigger.

[0087] Speech signals usually have more discontinuities, while noise usually exists continuously. As shown in Figure (1), the short-time energy of speech signals has more peaks and valleys, and the valleys are close to 0; while the short-time energy of noise signals has fewer peaks and valleys, and the valleys are not 0.

[0088] Assuming that the speech signal is x(n) and the noise signal is y(n), and that they have the same length, the normalized short-time energy is obtained from formula (5): and From formula (8), we can get Q x and Q y According to the characteristics of the peak and valley values ​​of the two signals, we can know that the speech signal The difference is large, the noise signal Therefore, Q x Closer to the maximum value, Q y Closer to the minimum, Qx and Q y There is the following relationship

[0089] Q x >Q y #(9)

[0090] That is, the Q of the speech signal x Greater than the Q of the noise signal y Based on this relationship, it can be determined whether the separated result is a speech signal or a noise signal.

[0091] 5) The speaker inside the earmuff inputs the voice signal into the earmuff cavity to complete the voice retention.

[0092] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, the above is only a preferred embodiment of the present invention. Since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited to this. Any technician familiar with the technical field is within the technical scope disclosed by the present invention. For ordinary technicians in the technical field, changes or replacements that can be easily thought of should be covered within the protection scope of the present invention without departing from the principle of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A noise-reducing earmuff that retains speech, characterized in that: It includes earmuffs with a physical sound insulation structure. A sound pickup microphone sensor for collecting external sound signals is arranged outside the earmuffs. Inside the earmuffs, there are a microprocessor module, an audio driving circuit, a speaker, and a power supply module. The sound pickup microphone sensor is connected to the microprocessor module through an ADC interface. The microprocessor module is connected to the audio driving circuit through a DAC interface. The audio driving circuit is connected to the speaker through a filter and an amplifier.

2. A method for noise reduction while preserving speech, characterized in that It includes the following steps: 1) Use earmuffs with a physical sound insulation structure, and utilize the external sound pickup microphone sensor of the earmuffs to collect external sound signals, including noise signals and voice signals; 2) Use the convolutional blind source separation algorithm to separate the noise signal and the voice signal; 3) Judge whether the noise signal and the voice signal are completely separated: 3.1) Use the discrimination method based on cross-correlation to judge the similarity degree between the two signals. If the cross-correlation is lower than the set threshold, go to the next step. If the cross-correlation is higher than the set threshold, return to step 1); 3.2) Use the discrimination method based on autocorrelation to judge the correlation degree of a certain signal between different time points. If the autocorrelation is lower than the set threshold, go to the next step. If the autocorrelation is higher than the set threshold, return to step 1); 4) Use the discrimination method based on short-time energy to judge whether the separated result is a voice signal or a noise signal; 4.1) Obtain the discriminant formula, assuming that a certain length is L d The signal is x d (n), whose short-time energy is E nd (n), the short-time energy sum is S d , S d The formula is E nd (n) is normalized to get Its formula is set up The sum of The formula is As can be seen from formulas (4), (5), and (6) set up The sum of the squares is Q d , whose formula is 4.2) Assume that the two signals obtained after separation in step 2) are x(n) and y(n), respectively. Cut a length L from the two signals. d The band is substituted into the formula In the above equation, we get Q x and Q y ,like Q x >Q y #(9) Then judge that x(n) is a voice signal and y(n) is a noise signal; 5) The speaker inside the earmuffs inputs the voice signal into the earmuff cavity to complete voice retention.

3. The method for reducing noise of preserved speech according to claim 2, characterized in that: For the discrimination method based on cross-correlation in step 3.1), it is specifically as follows: Assume that the two separated signals are x(n) and y(n), the cross-correlation function R of the signals x(n) and y(n) is xy The calculation formula for (n) is In the formula, n represents the time offset; Take the absolute mean value of the cross-correlation of x(n) and y(n) as h, and set the cross-correlation threshold as H. If h < H is satisfied, it indicates that the noise and voice signals are successfully separated.

4. The method for reducing noise of preserved speech according to claim 2, characterized in that: For the discrimination method based on autocorrelation in step 3.2), it is specifically as follows: Assume that one of the separated signals is x(n), the autocorrelation function R of the signal x(n) is xx The calculation formula for (n) is In the formula, n represents the time offset; Take the absolute mean value of the autocorrelation as z, and set the autocorrelation threshold as Z. If z < Z is satisfied, it indicates that the noise and voice signals are successfully separated.

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