Filtering noise from an audio file

Question

Naser Je 2018-5-11

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链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/400244-filtering-noise-from-an-audio-file

回答： matt 2022-12-1

DSP.zip

I have a corrupted audio file which contains a message with very loud noise, and I should filter the noise as much as possible. I tried couple of filters, and I could eliminate some of the noises but it's not enough. P.S. The expected audio file is also there, which I plotted it to compare the filtered one with it. Both audio files and the code are provided in the zip file.

clc;
close all;
clear;
[Clean, Fs2] = audioread('expected.m4a');
[sample_data, sample_rate] = audioread('corrupted.m4a');
signal = medfilt1(sample_data,90);      % Applying median filter
%_______________________________________________________________________________%
Fs = sample_rate;                       % Sampling Frequency (Hz)
Fn = Fs/2;                              % Nyquist Frequency (Hz)
Wp = 1000/Fn;                           % Passband Frequency (Normalised)
Ws = 1010/Fn;                           % Stopband Frequency (Normalised)
Rp =   1;                               % Passband Ripple (dB)
Rs = 150;                               % Stopband Ripple (dB)
[n,Ws] = cheb2ord(Wp,Ws,Rp,Rs);         % Filter Order
[z,p,k] = cheby2(n,Rs,Ws,'low');        % Filter Design
[soslp,glp] = zp2sos(z,p,k);    % Convert To Second-Order-Section For Stability
filtered_sound2 = filtfilt(soslp, glp, signal);
%_______________________________________________________________________________%
fs2_d = [filtered_sound2,filtered_sound2];
sound(fs2_d, sample_rate)
subplot(4,1,1);
plot(sample_data);            % Original Signal
title('Original Signal');
xlabel('Time (s)'); ylabel ('Amplitude');
subplot(4,1,2);
plot(filtered_sound2);          % Filtered output
title('Filtered Signal Double');
xlabel('Time (s)'); ylabel ('Amplitude');
subplot(4,1,3);
plot(fs2_d);         % Filtered output
title('Filtered Signal Double');
xlabel('Time (s)'); ylabel ('Amplitude');
subplot(4,1,4);
plot(Clean);                  % Expected output
title('Expected Signal');
xlabel('Time (s)'); ylabel ('Amplitude');

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Lochan Chitnis 2021-9-21

can someone please explain the above code

Mathieu NOE 2021-9-21

在 MATLAB Online 中打开

hi

just for the spectral analysis , I used this code. Seems the signal is burried in heavy broadband (white) noise plus two strong tones at 1000 and 2000 Hz

clc
clearvars
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% load signal
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% data
[signal,Fs] = audioread('corrupted.m4a');
% [signal,Fs] = audioread('expected.m4a');
[samples,channels] = size(signal);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% FFT parameters
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
NFFT = 256;    % 
OVERLAP = 0.75;
% spectrogram dB scale
spectrogram_dB_scale = 60;  % dB range scale (means , the lowest displayed level is XX dB below the max level)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 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;
%% decimate (if needed)
% NB : decim = 1 will do nothing (output = input)
decim = 1;
if decim>1
    for ck = 1:channels
    newsignal(:,ck) = decimate(signal(:,ck),decim);
    Fs = Fs/decim;
    end
   signal = newsignal;
end
samples = length(signal);
time = (0:samples-1)*1/Fs;
%%%%%% legend structure %%%%%%%%
for ck = 1:channels
    leg_str{ck} = ['Channel ' num2str(ck) ];
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% display 1 : time domain plot
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
figure(1),plot(time,signal);grid on
title(['Time plot  / Fs = ' num2str(Fs) ' Hz ']);
xlabel('Time (s)');ylabel('Amplitude');
legend(leg_str);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% display 2 : averaged FFT spectrum
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[freq, sensor_spectrum] = myfft_peak(signal,Fs,NFFT,OVERLAP);
% convert to dB scale (ref = 1)
sensor_spectrum_dB = 20*log10(sensor_spectrum);
% apply A weigthing if needed
if option_w == 1
    pondA_dB = pondA_function(freq);
    sensor_spectrum_dB = sensor_spectrum_dB+pondA_dB;
    my_ylabel = ('Amplitude (dB (A))');
else
    my_ylabel = ('Amplitude (dB (L))');
end
figure(2),plot(freq,sensor_spectrum_dB);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(my_ylabel);
legend(leg_str);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 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 : 
    % saturation_dB = 60;  % dB range scale (means , 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

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Answer 1

matt 2022-12-1

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/400244-filtering-noise-from-an-audio-file#answer_1117762

在 MATLAB Online 中打开

Limit bandwidth to region of highest signal strength in clean audio.

[Clean, Fs2] = audioread('expected.m4a');
[sample_data, sample_rate] = audioread('corrupted.m4a');
Fs = sample_rate;                      
d1 = designfilt('bandpassiir','FilterOrder',20, ...
         'HalfPowerFrequency1',100,'HalfPowerFrequency2',600, ...
         'SampleRate',Fs);
N = length(sample_data);
y1 = filtfilt(d1,sample_data);
%
Audio2 = audioplayer(y1,Fs);
play(Audio2);
f = linspace(0,Fs,N);
figure;
plot(f,abs(fft(sample_data)))
hold on
plot(f,abs(fft(Clean)))
plot(f,abs(fft(y1)))