├── README.md ├── 移频法 ├── TIMIT.WAV ├── ahc_freq_shift.m ├── man.wav ├── man_howling.wav ├── man_howling_suppression.wav ├── path.txt ├── readme.docx ├── readme.txt ├── timit_howling.wav ├── timit_howling_suppression.wav ├── 代码流程图.vsd ├── 未处理信号和啸叫抑制信号.png ├── 移频.png └── 算法逻辑图.vsd ├── 自适应法 ├── Application Program to Test Acoustic Feedback Reduction for Hearing Aids - simplified - LMS │ ├── Common │ │ ├── HA.m │ │ ├── HALMSadapt.m │ │ ├── HALMSinit.m │ │ ├── far.wav │ │ ├── far_howling.wav │ │ ├── far_howling_suppression.wav │ │ ├── fb.dat │ │ ├── near.WAV │ │ ├── near_howling.wav │ │ ├── near_howling_suppression.wav │ │ ├── sp_c2e2.wav │ │ ├── sp_c2e2_howling.wav │ │ └── sp_c2e2_howling_suppression.wav │ ├── Figure.png │ ├── Figure1.png │ ├── Figure2.png │ ├── Figure3.png │ ├── Figure4.png │ ├── Figure5.png │ ├── Steady-state analysis of continuous adaptation in acoustic feedback reduction systems for hearing-aids.pdf │ ├── readme.docx │ ├── 代码流程图.vsd │ ├── 算法逻辑图.vsd │ └── 说明.txt ├── Application Program to Test Acoustic Feedback Reduction for Hearing Aids - simplified - NLMS │ ├── Common │ │ ├── HA.m │ │ ├── HANLMSadapt.m │ │ ├── HANLMSinit.m │ │ ├── far.wav │ │ ├── far_howling.wav │ │ ├── far_howling_suppression.wav │ │ ├── fb.dat │ │ ├── near.WAV │ │ ├── near_howling.wav │ │ └── near_howling_suppression.wav │ ├── Figure.png │ ├── Figure1.png │ ├── Figure2.png │ ├── Figure3.png │ ├── Figure4.png │ ├── Figure5.png │ ├── Steady-state analysis of continuous adaptation in acoustic feedback reduction systems for hearing-aids.pdf │ ├── readme.docx │ ├── 代码流程图.vsd │ ├── 算法逻辑图.vsd │ └── 说明.txt └── 自适应啸叫抑制.docx └── 陷波法 ├── TIMIT.WAV ├── ahc_notch.m ├── man.wav ├── man_howling.wav ├── man_howling_suppression.wav ├── path.txt ├── readme.docx ├── readme.txt ├── timit_howling.wav ├── 代码流程图.vsd ├── 未处理信号和啸叫抑制信号.png ├── 算法逻辑图.vsd ├── 陷波.png └── 陷波器组的频响.png /README.md: -------------------------------------------------------------------------------- 1 | # Speech-processing 2 | howling suppression, 啸叫抑制, 声反馈抑制 3 | - 三种啸叫抑制方法：移频法、陷波法和自适应法。 4 | - 算法逻辑图、代码流程图，测试文档及相关资料。 5 | -------------------------------------------------------------------------------- /移频法/TIMIT.WAV: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Mathilda11/Speech-processing/032954fe95b6d1817bc47dfcef2a64030c53fab9/移频法/TIMIT.WAV -------------------------------------------------------------------------------- /移频法/ahc_freq_shift.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Mathilda11/Speech-processing/032954fe95b6d1817bc47dfcef2a64030c53fab9/移频法/ahc_freq_shift.m -------------------------------------------------------------------------------- /移频法/man.wav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Mathilda11/Speech-processing/032954fe95b6d1817bc47dfcef2a64030c53fab9/移频法/man.wav -------------------------------------------------------------------------------- /移频法/man_howling.wav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Mathilda11/Speech-processing/032954fe95b6d1817bc47dfcef2a64030c53fab9/移频法/man_howling.wav -------------------------------------------------------------------------------- /移频法/man_howling_suppression.wav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Mathilda11/Speech-processing/032954fe95b6d1817bc47dfcef2a64030c53fab9/移频法/man_howling_suppression.wav -------------------------------------------------------------------------------- /移频法/path.txt: -------------------------------------------------------------------------------- 1 | 0.000266 2 | -0.002012 3 | 0.000897 4 | -0.000504 5 | 0.000234 6 | -0.000172 7 | -0.000203 8 | 0.000293 9 | -0.001131 10 | -0.006891 11 | -0.135781 12 | -0.654303 13 | -1.332799 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0.000041 1953 | -0.000120 1954 | -0.000437 1955 | -0.000605 1956 | -0.000729 1957 | -0.001025 1958 | -0.001616 1959 | -0.002021 1960 | -0.002164 1961 | -0.001788 1962 | -0.001079 1963 | -0.000368 1964 | 0.000299 1965 | 0.000925 1966 | 0.001163 1967 | 0.000677 1968 | 0.000112 1969 | -0.000417 1970 | -0.001134 1971 | -0.001444 1972 | -0.001344 1973 | -0.001023 1974 | -0.000962 1975 | -0.000914 1976 | -0.000833 1977 | -0.000767 1978 | -0.000669 1979 | -0.000453 1980 | 0.000041 1981 | 0.000542 1982 | 0.000991 1983 | 0.001149 1984 | 0.001256 1985 | 0.001200 1986 | 0.000925 1987 | 0.000855 1988 | 0.001224 1989 | 0.001613 1990 | 0.001802 1991 | 0.001809 1992 | 0.001482 1993 | 0.000894 1994 | 0.000533 1995 | 0.000619 1996 | 0.001037 1997 | 0.001158 1998 | 0.001139 1999 | 0.001003 2000 | 0.000757 2001 | -------------------------------------------------------------------------------- /移频法/readme.docx: -------------------------------------------------------------------------------- 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Program to Test Acoustic Feedback Reduction for Hearing Aids - simplified - LMS/Common/HALMSadapt.m: -------------------------------------------------------------------------------- 1 | function [yn,en,yfb,S] = HALMSadapt(un,S) 2 | 3 | % HALMSadapt Adaptive FIR Filter with LMS Algorithm for Hearing Aids 4 | % 5 | % The history of output, square error and coefficients of 6 | % FIR filters are passed out to extenal. Refer to Fig. 1.9 7 | % 8 | % Arguments: 9 | % un Input signal to hearing aids 10 | % S Adptive filter parameters as defined in HALMSinit.m 11 | % yn History of output signal 12 | % en History of error signal 13 | % yfb History of feedback path signal 14 | % 15 | % 16 | % by Lee, Gan, and Kuo, 2008 17 | % Subband Adaptive Filtering: Theory and Implementation 18 | % Publisher: John Wiley and Sons, Ltd 19 | 20 | mu = S.step; % Step size for the LMS algorithm 21 | w = S.coeffs; % Weight vector of FIR filter 22 | M = length(S.coeffs); % Length of FIR filter 23 | fftap = zeros(1,M); % Feedforward taps 24 | M_fb = length(S.fb); % Length of feedback filter 25 | fbtap = zeros(1,M_fb); % Feedback taps 26 | delay = S.delay; 27 | 28 | ITER = length(un); % Length of input sequence 29 | yn = zeros(1,ITER); % Initialize output sequence to zero 30 | en = zeros(1,ITER); % Initialize error sequence to zero 31 | 32 | en(1) = un(1); % and let the 1st sample as 1st input sample 33 | yfb = zeros(1,ITER); % Initialize output of feedback path to zero 34 | u = zeros(1,ITER); % Initialize input of adaptive filter to zero 35 | u1 = zeros(1,ITER); % Initialize input of feedforward path to zero 36 | 37 | for n = 1:ITER 38 | 39 | if n > (delay+1) 40 | u(n) = S.ff(end)*en(n-delay-1); 41 | else 42 | u(n) = 0; 43 | end 44 | 45 | for k = 1:1:M_fb-1; % Updates of tapped-delay line for feedback plant 46 | fbtap(M_fb-k+1) = fbtap(M_fb-k); 47 | end 48 | fbtap(1) = un(n); 49 | yfb(n) = S.fb'*fbtap'; 50 | 51 | %if n >delay % Delayed input before adaptive filtering 52 | % u(n) = u1(n-delay); 53 | % else 54 | % u(n) = 0; 55 | % end 56 | 57 | for k = 1:1:M-1; % Updates of tapped-delay line of adaptive filter 58 | fftap(M-k+1) = fftap(M-k); 59 | end 60 | fftap(1) = un(n); 61 | yn(n) = w'*fftap'; % Compute output by inner product 62 | en(n) = un(n)+yfb(n)-yn(n); % Compute error signal 63 | w = w + (mu*en(n))*fftap'; % LMS algorithm 64 | S.iter = S.iter + 1; 65 | end 66 | 67 | S.coeffs = w; % Coefficient values at final iteration 68 | -------------------------------------------------------------------------------- /自适应法/Application Program to Test Acoustic Feedback Reduction for Hearing Aids - simplified - LMS/Common/HALMSinit.m: -------------------------------------------------------------------------------- 1 | function S = HALMSinit(w0,mu,fb,ff,delay) 2 | 3 | % HALMSinit Initialize Parameter Structure of the LMS Algorithm 4 | % for Hearing Aids. Refer to Fig. 1.9 5 | % 6 | % Arguments: 7 | % w0 Coefficients of FIR filter at start (@n=1) 8 | % mu Step size for LMS algorithm 9 | % ff Feedforward path 10 | % fb Feedback path 11 | % delay Delay to adaptive filter 12 | % 13 | % by Lee, Gan, and Kuo, 2008 14 | % Subband Adaptive Filtering: Theory and Implementation 15 | % Publisher: John Wiley and Sons, Ltd 16 | 17 | 18 | % Assign structure fields 19 | 20 | S.coeffs = w0(:); % Weight (column) vector of FIR filter 21 | S.step = mu; % Step size of the LMS algorithm 22 | S.iter = 0; % Iteration count 23 | S.ff = ff; % Feedforward path 24 | S.fb = fb; % Feedback path 25 | S.delay = delay; % Delay 26 | S.AdaptStart = length(w0); % Running effect of adaptive filter 27 | 28 | -------------------------------------------------------------------------------- /自适应法/Application Program to Test Acoustic Feedback Reduction for Hearing Aids - simplified - LMS/Common/far.wav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Mathilda11/Speech-processing/032954fe95b6d1817bc47dfcef2a64030c53fab9/自适应法/Application Program to Test Acoustic Feedback Reduction for Hearing Aids - 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simplified - LMS/Common/far_howling_suppression.wav -------------------------------------------------------------------------------- /自适应法/Application Program to Test Acoustic Feedback Reduction for Hearing Aids - simplified - LMS/Common/fb.dat: -------------------------------------------------------------------------------- 1 | 2.0000000e-003 2 | -2.0000000e-003 3 | 5.8000000e-002 4 | -7.8000000e-002 5 | 6.1000000e-002 6 | -2.8000000e-002 7 | 0.0000000e+000 8 | -8.0000000e-003 9 | 4.3000000e-002 10 | -2.7000000e-002 11 | 1.3000000e-002 12 | -1.3000000e-002 13 | 2.1000000e-002 14 | -1.2000000e-002 15 | 1.2000000e-002 16 | -1.3000000e-002 17 | 1.2500000e-002 18 | -1.5000000e-002 19 | 1.2200000e-002 20 | -9.0000000e-003 21 | 1.2000000e-002 22 | -1.0000000e-002 23 | 1.0000000e-002 24 | -6.0000000e-003 25 | 6.0000000e-003 26 | -8.0000000e-003 27 | 6.0000000e-003 28 | -6.0000000e-003 29 | 4.0000000e-003 30 | -4.0000000e-003 31 | 3.0000000e-003 32 | -4.2000000e-003 33 | 3.5000000e-003 34 | -2.5000000e-003 35 | 2.5000000e-003 36 | -2.5000000e-003 37 | 3.0000000e-003 38 | -2.0000000e-003 39 | 2.0000000e-003 40 | -1.8000000e-003 41 | 2.0000000e-003 42 | -1.8000000e-003 43 | 1.8000000e-003 44 | -1.4000000e-003 45 | 1.4000000e-003 46 | -1.3000000e-003 47 | 1.3000000e-003 48 | -1.2000000e-003 49 | 1.2000000e-003 50 | -1.1000000e-003 51 | 1.1000000e-003 52 | -1.0000000e-003 53 | 1.0000000e-003 54 | -9.0000000e-004 55 | 9.0000000e-004 56 | -8.0000000e-004 57 | 8.0000000e-004 58 | -7.0000000e-004 59 | 7.0000000e-004 60 | -6.0000000e-004 61 | 6.0000000e-004 62 | -5.0000000e-004 63 | 5.0000000e-004 64 | -4.0000000e-004 65 | 4.0000000e-004 66 | -3.0000000e-004 67 | 3.0000000e-004 68 | -2.0000000e-004 69 | 2.0000000e-004 70 | -1.0000000e-004 71 | 1.0000000e-004 72 | -8.0000000e-005 73 | 8.0000000e-005 74 | -6.0000000e-005 75 | 6.0000000e-005 76 | -4.0000000e-005 77 | 4.0000000e-005 78 | -2.0000000e-005 79 | 2.0000000e-005 80 | -1.0000000e-005 81 | 1.0000000e-005 82 | 5.0000000e-005 83 | 5.0000000e-005 84 | 5.0000000e-005 85 | 5.0000000e-005 86 | 5.0000000e-005 87 | 5.0000000e-005 88 | 5.0000000e-005 89 | 5.0000000e-005 90 | 5.0000000e-005 91 | 5.0000000e-005 92 | 5.0000000e-005 93 | 5.0000000e-005 94 | 5.0000000e-005 95 | 5.0000000e-005 96 | 5.0000000e-005 97 | 5.0000000e-005 98 | 5.0000000e-005 99 | 5.0000000e-005 100 | 5.0000000e-005 101 | -------------------------------------------------------------------------------- /自适应法/Application Program to Test Acoustic Feedback Reduction for Hearing Aids - 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simplified - NLMS/Common/HANLMSadapt.m: -------------------------------------------------------------------------------- 1 | function [yn,en,yfb,S] = HANLMSadapt(un,S) 2 | 3 | % HANLMSadapt NLMS Algorithm for Hearing Aids 4 | % 5 | % Perform over the entire length of input sequence for hearing 6 | % aids. The history of output, square error and coefficients of 7 | % FIR filters are passed out to extenal. Refer to Fig. 1.9. 8 | % 9 | % Parameters: 10 | % un Input signal 11 | % S Adptive filter parameters as defined in HANLMSinit.m 12 | % yn History of output signal 13 | % en History of error signal 14 | % yfb History of feedback path signal 15 | % 16 | % by Lee, Gan, and Kuo, 2008 17 | % Subband Adaptive Filtering: Theory and Implementation 18 | % Publisher: John Wiley and Sons, Ltd 19 | 20 | mu = S.step; % Step size of NLMS algorithm 21 | w = S.coeffs; % Weight vector of FIR filter 22 | M = length(S.coeffs); % Length of FIR filter 23 | fftap = zeros(1,M); % Feedforward taps 24 | M_fb = length(S.fb); % Length of feedback filter 25 | fbtap = zeros(1,M_fb); % Feedback taps 26 | delay = S.delay; 27 | 28 | ITER = length(un); % Length of input sequence 29 | yn = zeros(1,ITER); % Initialize output sequence to zero 30 | en = zeros(1,ITER); % Initialize error sequence to zero 31 | en(1) = un(1); % and let the 1st sample as 1st input sample 32 | yfb = zeros(1,ITER); % Initialize output of feedback path to zero 33 | u = zeros(1,ITER); % Initialize input of adaptive filter to zero 34 | u1 = zeros(1,ITER); % Initialize input of feedforward path to zero 35 | alpha = S.alpha; 36 | 37 | 38 | for n = 1:ITER 39 | if n > (delay+1) 40 | u(n) = S.ff(end)*en(n-delay-1); 41 | else 42 | u(n) = 0; 43 | end 44 | 45 | for k = 1:1:M_fb-1; % Updates of tapped-delay line for feedback plant 46 | fbtap(M_fb-k+1) = fbtap(M_fb-k); 47 | end 48 | fbtap(1) = un(n); 49 | yfb(n) = S.fb'*fbtap'; 50 | 51 | %if n >delay % Delayed input before adaptive filtering 52 | % u(n) = u1(n-delay); 53 | %else 54 | % u(n) = 0; 55 | %end 56 | for k = 1:1:M-1; % Updates of tapped-delay line of adaptive filter 57 | fftap(M-k+1) = fftap(M-k); 58 | end 59 | fftap(1) = un(n); 60 | yn(n) = w'*fftap'; % Compute filter output by inner product 61 | en(n) = un(n)+yfb(n)-yn(n); % Compute error signal 62 | w = w + ((mu*en(n))/(fftap*fftap'+alpha))*fftap'; % NLMS algorithm 63 | S.iter = S.iter + 1; 64 | end 65 | 66 | 67 | S.coeffs = w; % Coefficient values at final iteration 68 | -------------------------------------------------------------------------------- /自适应法/Application Program to Test Acoustic Feedback Reduction for Hearing Aids - simplified - NLMS/Common/HANLMSinit.m: -------------------------------------------------------------------------------- 1 | function S = HANLMSinit(w0,mu,fb,ff,delay) 2 | 3 | % HANLMSinit Initialize Parameter Structure of the NLMS Algorithm 4 | % for Hearing Aids. Refer to Fig. 1.9 5 | % 6 | % Arguments: 7 | % w0 Coefficients of FIR filter at start (@n=1) 8 | % mu Step size for the NLMS algorithm 9 | % ff Feedforward path 10 | % fb Feedback path 11 | % delay Delay to adaptive filter 12 | % 13 | % by Lee, Gan, and Kuo, 2008 14 | % Subband Adaptive Filtering: Theory and Implementation 15 | % Publisher: John Wiley and Sons, Ltd 16 | 17 | 18 | % Assign structure fields 19 | 20 | S.coeffs = w0(:); % Weight (column) vector of FIR filter 21 | S.step = mu; % Step size of the LMS algorithm 22 | S.iter = 0; % Iteration count 23 | S.alpha = 0.001; % A small constant to avoid divided by zero 24 | S.ff = ff; % Feedforward path 25 | S.fb = fb; % Feedback path 26 | S.delay = delay; % Delay 27 | S.AdaptStart= length(w0); % Running effect of adaptive filter 28 | 29 | -------------------------------------------------------------------------------- /自适应法/Application Program to Test Acoustic Feedback Reduction for Hearing Aids - simplified - NLMS/Common/far.wav: 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