Advanced processing of lung sound (LS) recording is a significant means to separate heart sounds (HS) and combined low frequency noise from instruments (NI), with saving its characteristics. This paper proposes a new ...Advanced processing of lung sound (LS) recording is a significant means to separate heart sounds (HS) and combined low frequency noise from instruments (NI), with saving its characteristics. This paper proposes a new method of LS filtering which separates HS and NI simultaneously. It focuses on the application of least mean squares (LMS) algorithm with adaptive noise cancelling (ANC) technique. The second step of the new method is to modulate the reference input r1(n) of LMS-ANC to acquiesce combining HS and NI signals. The obtained signal is removed from primary signal (original lung sound recording-LS). The original signal is recorded from subjects and derived HS from it and it is modified by a band pass filter. NI is simulated by generating approximately periodic white gaussian noise (WGN) signal. The LMS-ANC designed algorithm is controlled in order to determine the optimum values of the order L and the coefficient convergence μ. The output results are measured using power special density (PSD), which has shown the effectiveness of our suggested method. The result also has shown visual difference PSD (to) normal and abnormal LS recording. The results show that the method is a good technique for heart sound and noise reduction from lung sounds recordings simultaneously with saving LS characteristics.展开更多
AIM:To explore the more accurate lung sounds auscultation technology in high battlefield noise environment.METHODS: In this study, we restrain high background noise using a new method-adaptive noise canceling based on...AIM:To explore the more accurate lung sounds auscultation technology in high battlefield noise environment.METHODS: In this study, we restrain high background noise using a new method-adaptive noise canceling based on independent component analysis (ANC-ICA), the method, by incorporating both second-order and higher-order statistics can remove noise components of the primary input signal based on statistical independence.RESULTS:The algorithm retained the local feature of lung sounds while eliminating high background noise, and performed more effectively than the conventional LMS algorithm.CONCLUSION:This method can cancel high battlefield noise of lung sounds effectively thus can help diagnose lung disease more accurately.展开更多
Daily, we experience the effects of audio noise, which contaminates the original information bearing signal with noise from its surrounding environment. This paper focuses on real-time hardware implementation of multi...Daily, we experience the effects of audio noise, which contaminates the original information bearing signal with noise from its surrounding environment. This paper focuses on real-time hardware implementation of multi-tap adaptive noise cancellation （ANC） system by using the least mean square （LMS） algorithm on TMS320C6713 to remove undesired noise from a received signal for various audio related applications. Three different experiments are carried out by considering different audio inputs to test the efficiency of the designed ANC system. The ＇C＇ code implementation of LMS algorithm is introduced and simulated in code composer studio （CCS）, then realized on the digital signal processor （DSP） C6713. The 300 Hz, 500 Hz, 800 Hz, 1 kHz and 3 kHz of tone signals and male speech signal are used as the reference inputs to trace the noise of signal until it is eliminated. The performance of ANC system is studied in terms of convergence speed, order of the filter and signal-to-noise ratio （SNR）. The experimentam results demonstrate that the designed system shows a consider- able improvement in SNR.展开更多
In issues like hearing impairment,speech therapy and hearing aids play a major role in reducing the impairment.Removal of noise signals from speech signals is a key task in hearing aids as well as in speech therapy.Du...In issues like hearing impairment,speech therapy and hearing aids play a major role in reducing the impairment.Removal of noise signals from speech signals is a key task in hearing aids as well as in speech therapy.During the transmission of speech signals,several noise components contaminate the actual speech components.This paper addresses a new adaptive speech enhancement(ASE)method based on a modified version of singular spectrum analysis(MSSA).The MSSA generates a reference signal for ASE and makes the ASE is free from feeding reference component.The MSSA adopts three key steps for generating the reference from the contaminated speech only.These are decomposition,grouping and reconstruction.The generated reference is taken as a reference for variable size adaptive learning algorithms.In this work two categories of adaptive learning algorithms are used.They are step variable adaptive learning(SVAL)algorithm and time variable step size adaptive learning(TVAL).Further,sign regressor function is applied to adaptive learning algorithms to reduce the computational complexity of the proposed adaptive learning algorithms.The performance measures of the proposed schemes are calculated in terms of signal to noise ratio improvement(SNRI),excess mean square error(EMSE)and misadjustment(MSD).For cockpit noise these measures are found to be 29.2850,-27.6060 and 0.0758 dB respectively during the experiments using SVAL algorithm.By considering the reduced number of multiplications the sign regressor version of SVAL based ASE method is found to better then the counter parts.展开更多
本文应用RLS-ANC(recursive least squares adaptive noise cancellation)自适应滤波方法提取胎儿心电(FECG)信号。该方法采用RLS-ANC自适应滤波消除母亲心电,提取胎儿心电信号。实验结果表明,本方法适应非平稳信号的能力强,收敛速度快...本文应用RLS-ANC(recursive least squares adaptive noise cancellation)自适应滤波方法提取胎儿心电(FECG)信号。该方法采用RLS-ANC自适应滤波消除母亲心电,提取胎儿心电信号。实验结果表明,本方法适应非平稳信号的能力强,收敛速度快,提取效果好于NLMS(normalizedleast mean squares)算法。展开更多
Aimed at the problem of adaptive noise canceling(ANC),three implementary algorithms which are least mean square(LMS) algorithm,recursive least square(RLS) algorithm and fast affine projection(FAP) algorithm,have been ...Aimed at the problem of adaptive noise canceling(ANC),three implementary algorithms which are least mean square(LMS) algorithm,recursive least square(RLS) algorithm and fast affine projection(FAP) algorithm,have been researched.The simulations were made for the performance of these algorithms.The extraction of fetal electrocardiogram(FECG) is applied to compare the application effect of the above algorithms.The proposed FAP algorithm has obvious advantages in computational complexity,convergence speed and steadystate error.展开更多
The adaptive algorithm used for echo cancellation(EC) system needs to provide 1) low misadjustment and 2) high convergence rate. The affine projection algorithm(APA) is a better alternative than normalized least mean ...The adaptive algorithm used for echo cancellation(EC) system needs to provide 1) low misadjustment and 2) high convergence rate. The affine projection algorithm(APA) is a better alternative than normalized least mean square(NLMS) algorithm in EC applications where the input signal is highly correlated. Since the APA with a constant step-size has to make compromise between the performance criteria 1) and 2), a variable step-size APA(VSS-APA) provides a more reliable solution. A nonparametric VSS-APA(NPVSS-APA) is proposed by recovering the background noise within the error signal instead of cancelling the a posteriori errors. The most problematic term of its variable step-size formula is the value of background noise power(BNP). The power difference between the desired signal and output signal, which equals the power of error signal statistically, has been considered the BNP estimate in a rough manner. Considering that the error signal consists of background noise and misalignment noise, a precise BNP estimate is achieved by multiplying the rough estimate with a corrective factor. After the analysis on the power ratio of misalignment noise to background noise of APA, the corrective factor is formulated depending on the projection order and the latest value of variable step-size. The new algorithm which does not require any a priori knowledge of EC environment has the advantage of easier controllability in practical application. The simulation results in the EC context indicate the accuracy of the proposed BNP estimate and the more effective behavior of the proposed algorithm compared with other versions of APA class.展开更多
文摘Advanced processing of lung sound (LS) recording is a significant means to separate heart sounds (HS) and combined low frequency noise from instruments (NI), with saving its characteristics. This paper proposes a new method of LS filtering which separates HS and NI simultaneously. It focuses on the application of least mean squares (LMS) algorithm with adaptive noise cancelling (ANC) technique. The second step of the new method is to modulate the reference input r1(n) of LMS-ANC to acquiesce combining HS and NI signals. The obtained signal is removed from primary signal (original lung sound recording-LS). The original signal is recorded from subjects and derived HS from it and it is modified by a band pass filter. NI is simulated by generating approximately periodic white gaussian noise (WGN) signal. The LMS-ANC designed algorithm is controlled in order to determine the optimum values of the order L and the coefficient convergence μ. The output results are measured using power special density (PSD), which has shown the effectiveness of our suggested method. The result also has shown visual difference PSD (to) normal and abnormal LS recording. The results show that the method is a good technique for heart sound and noise reduction from lung sounds recordings simultaneously with saving LS characteristics.
基金Supported by Obligatory Budget of Chine PLA in the "tenth-five years"(OIL077)
文摘AIM:To explore the more accurate lung sounds auscultation technology in high battlefield noise environment.METHODS: In this study, we restrain high background noise using a new method-adaptive noise canceling based on independent component analysis (ANC-ICA), the method, by incorporating both second-order and higher-order statistics can remove noise components of the primary input signal based on statistical independence.RESULTS:The algorithm retained the local feature of lung sounds while eliminating high background noise, and performed more effectively than the conventional LMS algorithm.CONCLUSION:This method can cancel high battlefield noise of lung sounds effectively thus can help diagnose lung disease more accurately.
文摘Daily, we experience the effects of audio noise, which contaminates the original information bearing signal with noise from its surrounding environment. This paper focuses on real-time hardware implementation of multi-tap adaptive noise cancellation （ANC） system by using the least mean square （LMS） algorithm on TMS320C6713 to remove undesired noise from a received signal for various audio related applications. Three different experiments are carried out by considering different audio inputs to test the efficiency of the designed ANC system. The ＇C＇ code implementation of LMS algorithm is introduced and simulated in code composer studio （CCS）, then realized on the digital signal processor （DSP） C6713. The 300 Hz, 500 Hz, 800 Hz, 1 kHz and 3 kHz of tone signals and male speech signal are used as the reference inputs to trace the noise of signal until it is eliminated. The performance of ANC system is studied in terms of convergence speed, order of the filter and signal-to-noise ratio （SNR）. The experimentam results demonstrate that the designed system shows a consider- able improvement in SNR.
文摘In issues like hearing impairment,speech therapy and hearing aids play a major role in reducing the impairment.Removal of noise signals from speech signals is a key task in hearing aids as well as in speech therapy.During the transmission of speech signals,several noise components contaminate the actual speech components.This paper addresses a new adaptive speech enhancement(ASE)method based on a modified version of singular spectrum analysis(MSSA).The MSSA generates a reference signal for ASE and makes the ASE is free from feeding reference component.The MSSA adopts three key steps for generating the reference from the contaminated speech only.These are decomposition,grouping and reconstruction.The generated reference is taken as a reference for variable size adaptive learning algorithms.In this work two categories of adaptive learning algorithms are used.They are step variable adaptive learning(SVAL)algorithm and time variable step size adaptive learning(TVAL).Further,sign regressor function is applied to adaptive learning algorithms to reduce the computational complexity of the proposed adaptive learning algorithms.The performance measures of the proposed schemes are calculated in terms of signal to noise ratio improvement(SNRI),excess mean square error(EMSE)and misadjustment(MSD).For cockpit noise these measures are found to be 29.2850,-27.6060 and 0.0758 dB respectively during the experiments using SVAL algorithm.By considering the reduced number of multiplications the sign regressor version of SVAL based ASE method is found to better then the counter parts.
文摘本文应用RLS-ANC(recursive least squares adaptive noise cancellation)自适应滤波方法提取胎儿心电(FECG)信号。该方法采用RLS-ANC自适应滤波消除母亲心电,提取胎儿心电信号。实验结果表明,本方法适应非平稳信号的能力强,收敛速度快,提取效果好于NLMS(normalizedleast mean squares)算法。
基金the National Key Technologies R&D Program (No. 2006BAI22B01)
文摘Aimed at the problem of adaptive noise canceling(ANC),three implementary algorithms which are least mean square(LMS) algorithm,recursive least square(RLS) algorithm and fast affine projection(FAP) algorithm,have been researched.The simulations were made for the performance of these algorithms.The extraction of fetal electrocardiogram(FECG) is applied to compare the application effect of the above algorithms.The proposed FAP algorithm has obvious advantages in computational complexity,convergence speed and steadystate error.
文摘The adaptive algorithm used for echo cancellation(EC) system needs to provide 1) low misadjustment and 2) high convergence rate. The affine projection algorithm(APA) is a better alternative than normalized least mean square(NLMS) algorithm in EC applications where the input signal is highly correlated. Since the APA with a constant step-size has to make compromise between the performance criteria 1) and 2), a variable step-size APA(VSS-APA) provides a more reliable solution. A nonparametric VSS-APA(NPVSS-APA) is proposed by recovering the background noise within the error signal instead of cancelling the a posteriori errors. The most problematic term of its variable step-size formula is the value of background noise power(BNP). The power difference between the desired signal and output signal, which equals the power of error signal statistically, has been considered the BNP estimate in a rough manner. Considering that the error signal consists of background noise and misalignment noise, a precise BNP estimate is achieved by multiplying the rough estimate with a corrective factor. After the analysis on the power ratio of misalignment noise to background noise of APA, the corrective factor is formulated depending on the projection order and the latest value of variable step-size. The new algorithm which does not require any a priori knowledge of EC environment has the advantage of easier controllability in practical application. The simulation results in the EC context indicate the accuracy of the proposed BNP estimate and the more effective behavior of the proposed algorithm compared with other versions of APA class.
