trouble making a notchfilter to remove a tone out of a wavefile

조회 수: 2 (최근 30일)
Jacob
Jacob 2023년 11월 29일
댓글: Mathieu NOE 2023년 12월 1일
I am trying to write a matlab code to design a notchfilter to filter a tone out of a wav.file but I am having trouble getting the filter to work this code below is supposed to determine the frequencies of the audio in the wav.file and filter the tone out of the sunshinesquare.wav file. however when I go to run the program it plays the audio file with the tone in it still. I can't figure out what is wrong with it any suggestions/help is greatly appreciated.
My code is below
[sound, fs] = audioread('SunshineSquare.wav');
soundsc(sound,fs)
figure(1)
specgram(sound,fs)
p1 = 2 * pi;
bb = poly([exp(1*p1/4) exp(-1*p1/4) exp(1*p1/2) exp(-1*p1/2)]);
aa = poly(0.9 * [exp(1*p1/4) exp(-1*p1/4) exp(1*p1/2) exp(-1*p1/2)]);
figure;
zplane(bb, aa);
title('Poles and Zeros Plot');
Ww = -p1:p1/100:p1;
HH1 = freqz(bb, 1, Ww);
HH2 = freqz(bb, aa, Ww);
figure;
plot(Ww, abs(HH1), 'b', 'LineWidth', 2);
hold on;
plot(Ww, abs(HH2), 'r', 'LineWidth', 2);
hold off;
legend('Zeros Only', 'Poles and Zeros');
xlabel('Frequency');
ylabel('Magnitude');
title('Frequency Response');
audiowrite('processed_SunshineSquare.wav', y_processed, Fs_processed);
soundsc(y, Fs);

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Mathieu NOE
Mathieu NOE 2023년 11월 29일
편집: Mathieu NOE 2023년 11월 29일
hello
adapt this code to your needs
you can apply multiples notches , simply create the list of frequencies here
fc_notch = [9888; 10896]; % notch center frequencies
cheers
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% select filters to apply
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
option_notch = 1; % 0 = without notch filter , 1 = with notch filter
fc_notch = [9888; 10896]; % notch center frequencies
p = 0.98; % bandwith parameter (closer to 1 reduces the BW)
option_BPF = 1; % 0 = without bandpass filter , 1 = with bandpass filter
fc = 1e4; % Hz
bandwith = 1e4; % Hz
N_bpf = 4; % filter order
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% options
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% if you are dealing with acoustics, you may wish to have A weighted
% spectrums
% option_w = 0 : linear spectrum (no weighting dB (L) )
% option_w = 1 : A weighted spectrum (dB (A) )
option_w = 0;
% spectrogram dB scale
spectrogram_dB_scale = 80; % the lowest displayed level is XX dB below the max level
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% load signal
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[signal,Fs] = audioread('original.wav');
dt = 1/Fs;
[samples,channels] = size(signal);
% select channel (if needed)
channels = 1;
signal = signal(:,channels);
% time vector
time = (0:samples-1)*dt;
%% decimate (if needed)
% NB : decim = 1 will do nothing (output = input)
decim = 0;
if decim>1
Fs = Fs/decim;
for ck = 1:channels
newsignal(:,ck) = decimate(signal(:,ck),decim);
end
signal = newsignal;
end
signal_filtered = signal;
samples = length(signal);
time = (0:samples-1)*1/Fs;
%% notch filter section %%%%%%
% y(n)=G*[x(n)-2*cos(w0)*x(n-1)+x(n-2)]+[2*p cos(w0)*y(n-1)-p^2 y(n-2)]
if option_notch ~= 0
for ck =1:numel(fc_notch)
w0 = 2*pi*fc_notch(ck)/Fs;
% digital notch (IIR)
num1z=[1 -2*cos(w0) 1];
den1z=[1 -2*p*cos(w0) p^2];
% now let's filter the signal
signal_filtered = filter(num1z,den1z,signal_filtered);
end
end
%% band pass filter section %%%%%%
if option_BPF ~= 0
f_low = fc-0.5*bandwith;
f_high = fc+0.5*bandwith;
[b,a] = butter(N_bpf,2/Fs*[f_low f_high]);
signal_filtered = filter(b,a,signal_filtered);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% FFT parameters
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
NFFT = 1000; %
OVERLAP = 0.75; % percentage of overlap
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% display 1 : time domain plot
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
figure(1),
subplot(211),plot(time,signal,'b');grid on
title(['Time plot / Fs = ' num2str(Fs) ' Hz / raw data ']);
xlabel('Time (s)');ylabel('Amplitude');
subplot(212),plot(time,signal_filtered,'r');grid on
title(['Time plot / Fs = ' num2str(Fs) ' Hz / filtered data ']);
xlabel('Time (s)');ylabel('Amplitude');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% display 2 : averaged FFT spectrum
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[freq, spectrum_raw] = myfft_peak(signal,Fs,NFFT,OVERLAP);
[freq, spectrum_filtered] = myfft_peak(signal_filtered,Fs,NFFT,OVERLAP);
figure(2),plot(freq,20*log10(spectrum_raw),'b',freq,20*log10(spectrum_filtered),'r');grid on
df = freq(2)-freq(1); % frequency resolution
title(['Averaged FFT Spectrum / Fs = ' num2str(Fs) ' Hz / Delta f = ' num2str(df,3) ' Hz ']);
xlabel('Frequency (Hz)');ylabel('Amplitude (dB (L))');
legend('raw','filtered');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% display 3 : time / frequency analysis : spectrogram demo
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for ck = 1:channels
[sg,fsg,tsg] = specgram(signal(:,ck),NFFT,Fs,hanning(NFFT),floor(NFFT*OVERLAP));
% FFT normalisation and conversion amplitude from linear to dB (peak)
sg_dBpeak = 20*log10(abs(sg))+20*log10(2/length(fsg)); % NB : X=fft(x.*hanning(N))*4/N; % hanning only
% apply A weigthing if needed
if option_w == 1
pondA_dB = pondA_function(fsg);
sg_dBpeak = sg_dBpeak+(pondA_dB*ones(1,size(sg_dBpeak,2)));
my_title = ('Spectrogram (dB (A))');
else
my_title = ('Spectrogram (dB (L))');
end
% saturation of the dB range : the lowest displayed level is XX dB below the max level
min_disp_dB = round(max(max(sg_dBpeak))) - spectrogram_dB_scale;
sg_dBpeak(sg_dBpeak<min_disp_dB) = min_disp_dB;
% plots spectrogram
figure(2+ck);
imagesc(tsg,fsg,sg_dBpeak);colormap('jet');
axis('xy');colorbar('vert');grid on
df = fsg(2)-fsg(1); % freq resolution
title([my_title ' / Fs = ' num2str(Fs) ' Hz / Delta f = ' num2str(df,3) ' Hz / Channel : ' num2str(ck)]);
xlabel('Time (s)');ylabel('Frequency (Hz)');
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function pondA_dB = pondA_function(f)
% dB (A) weighting curve
n = ((12200^2*f.^4)./((f.^2+20.6^2).*(f.^2+12200^2).*sqrt(f.^2+107.7^2).*sqrt(f.^2+737.9^2)));
r = ((12200^2*1000.^4)./((1000.^2+20.6^2).*(1000.^2+12200^2).*sqrt(1000.^2+107.7^2).*sqrt(1000.^2+737.9^2))) * ones(size(f));
pondA = n./r;
pondA_dB = 20*log10(pondA(:));
end
function [freq_vector,fft_spectrum] = myfft_peak(signal, Fs, nfft, Overlap)
% FFT peak spectrum of signal (example sinus amplitude 1 = 0 dB after fft).
% Linear averaging
% signal - input signal,
% Fs - Sampling frequency (Hz).
% nfft - FFT window size
% Overlap - buffer percentage of overlap % (between 0 and 0.95)
[samples,channels] = size(signal);
% fill signal with zeros if its length is lower than nfft
if samples<nfft
s_tmp = zeros(nfft,channels);
s_tmp((1:samples),:) = signal;
signal = s_tmp;
samples = nfft;
end
% window : hanning
window = hanning(nfft);
window = window(:);
% compute fft with overlap
offset = fix((1-Overlap)*nfft);
spectnum = 1+ fix((samples-nfft)/offset); % Number of windows
% % for info is equivalent to :
% noverlap = Overlap*nfft;
% spectnum = fix((samples-noverlap)/(nfft-noverlap)); % Number of windows
% main loop
fft_spectrum = 0;
for i=1:spectnum
start = (i-1)*offset;
sw = signal((1+start):(start+nfft),:).*(window*ones(1,channels));
fft_spectrum = fft_spectrum + (abs(fft(sw))*4/nfft); % X=fft(x.*hanning(N))*4/N; % hanning only
end
fft_spectrum = fft_spectrum/spectnum; % to do linear averaging scaling
% one sidded fft spectrum % Select first half
if rem(nfft,2) % nfft odd
select = (1:(nfft+1)/2)';
else
select = (1:nfft/2+1)';
end
fft_spectrum = fft_spectrum(select,:);
freq_vector = (select - 1)*Fs/nfft;
end
  댓글 수: 13
Jacob
Jacob 2023년 12월 1일
Interesting yeah I heard the extra noise in it but I was going to roll with it because I could actually hear the audio and not the tone in it! But thank you I will use this! Thank you for your help!

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