A fast setting algorithm, device, medium and equipment for initial weight of environmental sound adaptive noise reduction filter for auscultation

Abstract

The application discloses a kind of fast setting algorithm, device, medium and equipment for initial weight of environmental sound adaptive noise reduction filter for auscultation;The algorithm simultaneously collects the impulse response of main and auxiliary channels in a quiet environment;With the impulse response of the auxiliary channel as the core, the impulse response of the main channel is deconvoluted, and the result obtained is the impulse response of the expected model of the adaptive filter;Finally, the order and initial weight of the adaptive filter are determined according to the impulse response obtained by deconvolution. The algorithm has small computational load, and can determine the initial weight and order of the adaptive filter online, suitable for application in pipeline mass production.

Description

Technical Field

[0001] The present invention relates to the technical field of medical measurement and signal processing, and in particular to an algorithm, device, medium and equipment for quickly setting the initial weights of an ambient sound adaptive noise reduction filter for auscultation. Background Art

[0002] Heart and respiratory sound signals are weak and easily interfered with by ambient noise during auscultation. Dual-channel adaptive filtering is a commonly used environmental noise reduction technology. Its application to body sound auscultation can effectively suppress interference from ambient noise.

[0003] Setting an appropriate filter order and initial weights can significantly shorten the convergence process of an adaptive filter and is crucial to the practicality of a noise reduction algorithm. Currently available methods address this issue by identifying the equivalent transfer function based on the cross-correlation function between the primary and secondary channel ambient noise, thereby determining the adaptive filter order and initial weights. However, this method requires constructing autocorrelation and cross-correlation matrices, as well as performing operations such as matrix inversion and singular value decomposition. This algorithm is complex and computationally intensive to implement, making it unsuitable for online parameter setting of factory-produced stethoscopes on the production line. Summary of the Invention

[0004] In order to overcome the shortcomings and deficiencies in the prior art, the purpose of the present invention is to provide an algorithm for quickly setting the initial weights of an adaptive noise reduction filter for ambient sound used for auscultation; this algorithm can be used to conveniently and quickly set the filter order and initial weights online on the production line.

[0005] In order to achieve the above purpose, Figure 2 As shown, the present invention uses the secondary channel ambient noise as the input signal and the main channel ambient noise signal as the output signal to identify the order and impulse response of the desired model of the dual-channel adaptive filter. Specifically, this is achieved through the following technical solutions: a fast setting algorithm for the initial weights of the ambient sound adaptive noise reduction filter for auscultation, characterized by: using an artificial mouth or a speaker to play a pulse signal as the ambient noise source signal, using a dual-channel digital stethoscope to collect the ambient noise signal, and the ambient noise signal collected by the main channel is N ′ 1(k), the ambient noise signal collected by the secondary channel is N ′ 2(k), the signals collected by the main and auxiliary channels are preprocessed using bandpass filtering to suppress low-frequency and high-frequency components, and the main channel filtered signal N1(k) and the auxiliary channel filtered signal N2(k) are obtained respectively; the impulse response sequence G of the expected model of the adaptive filter is estimated by deconvolution; the order and initial weights of the dual-channel adaptive filter can be determined based on G.

[0006] Specifically, the steps include:

[0007] S1 step, in a quiet environment, a pulse signal is played as ambient noise by artificial mouth or sound box, and the starting time of the pulse is recorded as zero time, i.e. k=0.

[0008] S2 step, the main channel ambient noise signal N ′ 1(k) and the secondary channel ambient noise signal N ′ 2(k) collected by the double-channel digital stethoscope under the excitation of the pulse signal are recorded.

[0009] S3 step, the main channel ambient noise signal N ′ 1(k) and the secondary channel ambient noise signal N ′ 2(k) are preprocessed by using a band-pass filter to suppress low-frequency and high-frequency components, and the main channel filtered signal N1(k) and the secondary channel filtered signal N2(k) are obtained. Preferably, the order of the band-pass filter is not less than 2, and the cutoff frequencies are f L ∈[20,80] Hz and f H ∈[1500,2000] Hz, respectively.

[0010] S4 step, after a period of time, the amplitudes of the signals N1(k) and N2(k) will converge within a certain threshold and will not exceed the threshold over time. The threshold corresponding to N1(k) is denoted as δ1, and the threshold corresponding to N2(k) is denoted as δ2; the time when the amplitude of N1(k) converges to the threshold δ1 is denoted as t1, and the time when the amplitude of N2(k) converges to the threshold δ2 is denoted as t2.

[0011] Preferably, δ1=c1max{|N1(k)|} and δ2=c2max{|N2(k)|}, and c1,c2∈[0.01,0.1].

[0012] The main channel impulse response is denoted as a vector p1=[N1(0)…N1(t1)], and the secondary channel impulse response is denoted as a vector p2=[N2(0)…N2(t2)].

[0013] S5 step, the inverse convolution of p1 is performed with p2 as the kernel, and the obtained result is the impulse response sequence G=[g0,g1,…,g j ,…] of the expected model of the adaptive filter; when j is greater than a certain value, the amplitude of g j will converge to a certain threshold and will not exceed the threshold over time.

[0014] The threshold is denoted as δ g , and the time when the amplitude of g j converges to the threshold δ g is denoted as t g , i.e. when j>t g , |gj |<δ g , then the order of the adaptive filter can be taken as t g +1, the initial value of the adaptive filter is

[0015] Preferably, take δ g =c g max{|g j |}, c g ∈[0.01,0.1].

[0016] The technical principle of the algorithm of the present invention is:

[0017] like Figure 2 As shown, the present invention uses the secondary channel ambient noise as the input signal and the main channel ambient noise signal as the output signal to identify the order and impulse response of the expected model of the digital stethoscope dual-channel adaptive filter.

[0018] An artificial mouth or speaker plays a pulse signal as the ambient noise source signal, and a dual-channel digital stethoscope is used to collect the ambient noise signal. The ambient noise signal collected by the main channel is N ′ 1(k), the ambient noise signal collected by the secondary channel is N ′ 2(k).

[0019] Depend on Figure 2 It can be seen that the impulse response sequence of the adaptive filter expectation model is It can be obtained by deconvolving the main channel impulse response with the secondary channel impulse response. Since the transfer function of the ideal adaptive filter should be consistent with the transfer function of the adaptive filter expectation model, the order of the adaptive filter can be taken as t g +1, and its initial value can be taken as

[0020] If the environmental noise signals N′1(k) and N′2(k) collected by the main and auxiliary channels of the digital stethoscope are directly used to calculate the impulse response sequence G, the obtained impulse response sequence may contain low-frequency oscillation components and their harmonics generated by the vibration of the stethoscope diaphragm. This component can easily lead to misjudgment of the desired model order and initial weights of the adaptive filter. Therefore, before calculating the order and initial weights, the environmental noise signals N′1(k) and N′2(k) collected by the main and auxiliary channels of the digital stethoscope need to be pre-processed with the same bandpass filter to suppress the low-frequency oscillation components and their high-frequency noise. Preferably, the order of the bandpass filter is not less than 2, and the cutoff frequency is f L ∈[20,80]Hz and f H ∈[1500,2000]Hz.

[0021] The application discloses an ambient sound self-adaptive noise reduction device for auscultation, which realizes the above-mentioned fast setting algorithm of initial weights of an ambient sound self-adaptive noise reduction filter for auscultation.

[0022] A storage medium, wherein the storage medium stores a computer program, and the computer program, when executed by a processor, causes the processor to execute the above-mentioned fast setting algorithm of initial weights of an ambient sound self-adaptive noise reduction filter for auscultation.

[0023] A computing device, comprising a processor and a memory for storing a processor-executable program, and the processor, when executing the program stored in the memory, realizes the above-mentioned fast setting algorithm of initial weights of an ambient sound self-adaptive noise reduction filter for auscultation.

[0024] Compared with the prior art, the application has the following advantages and beneficial effects:

[0025] 1. The application only needs to perform inverse convolution operation on the main and auxiliary channel signals, avoids operation of obtaining a signal correlation matrix and inverse operation of a high-order matrix, has small calculation amount, and can set weights and filter orders on line, and can be directly applied to pipeline batch production.

[0026] 2. The application can also be applied to a wearable body sound acquisition device; when the use environment changes, the device can automatically set initial weights on line by using the method of the application, and the noise reduction performance is improved. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 It is a flowchart of the algorithm of the application;

[0028] Figure 2 It is a calculation principle diagram of a transfer function G(s) of a double-channel adaptive filter expected model;

[0029] Figure 3 It is a main and auxiliary channel output signal waveform diagram measured when a pulse signal is played by using a sound in a quiet environment in embodiment one of the application;

[0030] Figure 4 It is a pulse response waveform of an adaptive filter expected model obtained by using the method of the application in embodiment one of the application. DETAILED DESCRIPTION

[0031] The application will be further described in detail below in combination with the drawings and specific embodiments.

[0032] Embodiment one

[0033] The embodiment is a fast setting algorithm for initial weight of environmental sound adaptive noise reduction filter for auscultation, which aims at solving the problem that the existing method is not suitable for setting initial parameters of auscultator on production line online due to large calculation amount, and quickly estimates the order and initial weight of adaptive filter according to the relationship between main and auxiliary channel impulse responses of auscultator.

[0034] To realize the fast setting algorithm for initial weight of environmental sound adaptive noise reduction filter for auscultation, the embodiment comprises the following steps:

[0035] Step 1: In a quiet environment, a pulse signal is played as environmental noise by artificial mouth or sound box, and the time when the pulse starts is recorded as zero time, i.e. k=0.

[0036] Step 2: The main channel environmental noise signal N'1(k) and the auxiliary channel environmental noise signal N'2(k) collected by the main and auxiliary channels of the double-channel digital auscultator under the excitation of the pulse signal are recorded.

[0037] Step 3: The main channel environmental noise signal N'1(k) and the auxiliary channel environmental noise signal N'2(k) are preprocessed by a band-pass filter to suppress low and high frequency components, and the main channel filtered signal N1(k) and the auxiliary channel filtered signal N2(k) are obtained. The order of the band-pass filter is 6, and the cutoff frequencies are f L =50Hz and f H =1500Hz.

[0038] Step 4: After a period of time, the amplitudes of the signals N1(k) and N2(k) will converge to a certain threshold and will not exceed the threshold over time. The threshold is determined according to the maximum amplitude of the signal, and the threshold δ1=0.1max{|N1(k)|} and δ2=0.1max{|N2(k)|} are set. The amplitude of N1(k) converges to the threshold δ1 after the 240th sampling period, and the amplitude of N2(k) converges to the threshold δ2 after the 400th sampling period. The main channel impulse response vector p1=[N1(0)...N1(239)] and the auxiliary channel impulse response vector p2=[N2(0)...N2(399)] are recorded.

[0039] Step 5: The p1 is deconvolved with p2 as the core, and the result is the impulse response sequence G=[g0,g1,...] of the expected model of the adaptive filter; δ j =0.1max{|g j |} is taken to determine the time t j =111 when the amplitude of g g converges to δ g , i.e. |g j |<δ g when j>111.

[0040] The order of the adaptive filter is 112, and the initial value is [g0, g1, …, g 111 ]。

[0041] The application discloses an environmental sound adaptive noise reduction device for auscultation, which realizes the fast setting algorithm of the initial weight of the environmental sound adaptive noise reduction filter for auscultation.

[0042] A storage medium, wherein the storage medium stores a computer program, and the computer program, when executed by a processor, causes the processor to execute the fast setting algorithm of the initial weight of the environmental sound adaptive noise reduction filter for auscultation.

[0043] A computing device, comprising a processor and a memory for storing a processor-executable program, and the processor, when executing the program stored in the memory, realizes the fast setting algorithm of the initial weight of the environmental sound adaptive noise reduction filter for auscultation.

[0044] The above-mentioned embodiments are the preferred embodiments of the application, but the embodiments of the application are not limited to the above-mentioned embodiments, and any changes, modifications, substitutions, combinations and simplifications made without departing from the spirit and principle of the application shall be equivalent replacement modes and shall be included in the protection scope of the application.

Claims

1. A fast algorithm for setting the initial weights of an adaptive noise reduction filter for ambient sound auscultation, characterized by The steps include: Step S1: In a quiet environment, use an artificial mouth or a speaker to play a pulse signal as environmental noise, and record the pulse start time as time zero, that is, k=0; Step S2, recording the main channel environmental noise signal N′1(k) and the auxiliary channel environmental noise signal N′2(k) collected by the main channel and the auxiliary channel respectively under the excitation of the pulse signal of the dual-channel digital stethoscope; Step S3: Use a bandpass filter to perform the same preprocessing on the main channel ambient noise signal N′1(k) and the secondary channel ambient noise signal N′2(k) to suppress low-frequency and high-frequency components, thereby obtaining the main channel filtered signal N1(k) and the secondary channel filtered signal N2(k), respectively. Step S4: After a period of time, the amplitudes of signals N1(k) and N2(k) converge to a certain threshold and will no longer exceed the threshold over time; the threshold corresponding to N1(k) is denoted as δ1, and the threshold corresponding to N2(k) is denoted as δ2; and the time when the amplitude of N1(k) converges to the threshold δ1 is denoted as t1, and the time when the amplitude of N2(k) converges to δ2 is denoted as t2; the main channel impulse response vector p1 = [N1(0)…N1(t1)], and the secondary channel impulse response vector p2 = [N2(0)…N2(t2)]; Step S5, deconvolve p1 with p2 as the kernel, and the result is the impulse response sequence G = [g0, g1, ..., g j ,…]; j is the sequence index of G. When j is greater than a certain value, g j The magnitude of will converge to a certain threshold and will no longer exceed the threshold over time; Let this threshold be δ g , and g j The amplitude converges to the threshold δ g The time is t g , that is, when j>t g When |g j |<δ g , then the order of the adaptive filter is t g +1, take its initial value take g =c g max{|g j |},c g ∈[0.01,0.1].

2. The algorithm for quickly setting initial weights of an adaptive noise reduction filter for auscultation of ambient sound according to claim 1, characterized in that: In step S3, the main channel ambient noise signal N′1(k) and the sub-channel ambient noise signal N′2(k) are preprocessed in the same manner using a bandpass filter to suppress low-frequency and high-frequency components, respectively, to obtain the main channel filtered signal N1(k) and the sub-channel filtered signal N2(k), respectively.

3. The algorithm for quickly setting initial weights of an adaptive noise reduction filter for auscultation of ambient sound according to claim 2, characterized in that: The order of the bandpass filter is not less than 2, and the cutoff frequency is f L ∈[20,80]Hz and f H ∈[1500,2000]Hz.

4. The algorithm for quickly setting initial weights of an adaptive noise reduction filter for auscultation of ambient sound according to claim 1, characterized in that: In step S5, the order and initial weights of the adaptive filter are determined as follows: Step S5, using the secondary channel impulse response vector p2 as the core, deconvolve the main channel impulse response vector p1, and the result is the impulse response sequence G = [g0, g1 ..., g j ,…]; j is the sequence index of G. When j is greater than a certain value, |g j |will converge to a certain threshold and will not exceed that threshold over time; Let this threshold be δ g , and |g j Converge to threshold δ g The time is t g , that is, when j>t g When |g j |<δ g , then the order of the adaptive filter is t g +1, take its initial value take g =c g max{g j },c g ∈[0.01,0.1].

5. The algorithm for quickly setting initial weights of an adaptive noise reduction filter for auscultation of ambient sound according to claim 4, characterized in that: The main channel impulse response vector p1 = [N1(0)…N1(t1)], where t1 is the moment when |N1(k)| converges to the threshold δ1, that is, when k>t1, |N1(k)|<δ1; the threshold δ1 = c1max{|N1(k)|}, and c1∈[0.01,0.1].

6. The algorithm for quickly setting initial weights of an adaptive noise reduction filter for auscultation of ambient sound according to claim 4, characterized in that: The secondary channel impulse response vector p2 = [N2(0)…N2(t2)], where t2 is the moment when |N2(k)| converges to the threshold δ2, that is, when k>t2, |N2(k)|<δ2; the threshold δ2 = c2max{|N2(k)|}, and c2∈[0.01,0.1].

7. The algorithm for quickly setting initial weights of an adaptive noise reduction filter for auscultation of ambient sound according to claim 5, characterized in that: The main channel impulse response vector p1=[N1(0)…N1(t1)] is used to determine the threshold δ1 at the time t1, whose value is δ1=c1max{|N1(k)|}, and c1∈[0.01,0.1].

8. The algorithm for quickly setting initial weights of an adaptive noise reduction filter for auscultation of ambient sound according to claim 6, characterized in that: The secondary channel impulse response vector p2=[N2(0)…N2(t2)] is used to determine the threshold δ2 at the time t2, whose value is δ2=c2max{|N2(k)|}, and c2∈[0.01,0.1].

9. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, enables the processor to execute the fast setting algorithm for initial weights of an ambient sound adaptive noise reduction filter for auscultation according to any one of claims 1 to 8.

10. A computing device comprising a processor and a memory for storing a program executable by the processor, characterized in that When the processor executes the program stored in the memory, the fast setting algorithm for initial weights of the ambient sound adaptive noise reduction filter for auscultation according to any one of claims 1 to 8 is implemented.

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