How to filter breath noise in audio?

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In the attachment are the original audio files and the MATLAB filter files used. I tried low-pass filtering and band-pass filtering. The effect is not obvious. This noise is mainly heavy breathing sound. How can I filter this breathing sound and save the speaking sound completely (Chinese or English)?
  5 Comments
wei sun
wei sun on 13 Jul 2022
remove or attenuate this noise.

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Accepted Answer

Mathieu NOE
Mathieu NOE on 13 Jul 2022
Edited: Mathieu NOE on 13 Jul 2022
hello
i opted for a strategy based on the spectrogram content. I noticed that the "breathing" sections are characterized by a strong spectrogram output below 100 Hz (red dots) which is not the case for the "speaking" sections
I worked on channel 1 as channel 2 is clipped (distorded)
so I simply reduced the volume (here - 30 dB) for the segments that goes from the local minima just before and after each red dot
(you can also put directly zero if you prefer - see options in the code)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% options
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% spectrogram dB scale
spectrogram_dB_scale = 80; % dB range scale (means , 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);
signal_filtered = signal;
% time vector
time = (0:samples-1)*dt;
%% decimate (if needed)
% NB : decim = 1 will do nothing (output = input)
decim = 40;
if decim>1
signal_decim = decimate(signal,decim);
Fs_decim = Fs/decim;
end
samples_decim = length(signal_decim);
time_decim = (0:samples_decim-1)*1/Fs_decim;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% FFT parameters
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
NFFT = 512; %
OVERLAP = 0.75;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% display : time / frequency analysis : spectrogram
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[sg,fsg,tsg] = specgram(signal_decim,NFFT,Fs_decim,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
% saturation of the dB range :
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);
imagesc(tsg,fsg,sg_dBpeak);colormap('jet');
axis('xy');colorbar('vert');grid on
df = fsg(2)-fsg(1); % freq resolution
title(['Spectrogram / Fs = ' num2str(Fs) ' Hz / Delta f = ' num2str(df,3) ' Hz ']);
xlabel('Time (s)');ylabel('Frequency (Hz)');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% extract SG (dB) values from 0 to 100 hz (loud level in this freq range is
% breath sound
ind = find(fsg<=100);
fsg_breath = fsg(ind);
sg_dB_breath = sg_dBpeak(ind,:);
max_dB = max(sg_dB_breath,[],1);
max_dB = max_dB-min(max_dB); % shift the dB values to positive values for good working islocalmax
% select peaks above +25 dB and neighboring local mins
% find local maxima
[tf, P] = islocalmax(max_dB,'MinProminence',25);
x_peak = tsg(tf);
y_peak = max_dB(tf);
% find local minima
[tm, P] = islocalmin(max_dB);
x_min = tsg(tm);
y_min = max_dB(tm);
figure(3);plot(tsg,max_dB,x_peak,y_peak,'dr',x_min,y_min,'dk');
title('Spectrogram max dB value vs Time');
xlabel('Time (s)');ylabel('Max dB value');
% set to zero the data that are defined by the local mins just before
% and after the high peaks
for ck = 1:numel(x_peak)
% search x_min just before
dist = x_min - x_peak(ck);
ind_bef = find(dist<0,1,'last');
x_min_bef = x_min(ind_bef);
ind_aft = find(dist>0,1,'first');
x_min_aft = x_min(ind_aft);
% now zero time signal between these two time indexes
ind = find(time>=x_min_bef & time<=x_min_aft);
% signal_filtered(ind) = 0; % option 1 : zero
signal_filtered(ind) = signal_filtered(ind)/30 ; % option 2 : 30 dB attenuation
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% display : time domain plot
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
figure(1),
subplot(2,1,1),plot(time,signal,'b');grid on
title(['Time plot / Fs = ' num2str(Fs) ' Hz / raw data ']);
xlabel('Time (s)');ylabel('Amplitude');
subplot(2,1,2),plot(time,signal_filtered,'b');grid on
title(['Time plot / Fs = ' num2str(Fs) ' Hz / filtered data ']);
xlabel('Time (s)');ylabel('Amplitude');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% export signal
audiowrite('filtered.wav',signal_filtered,Fs); % audiowrite(filename,y,Fs,varargin)
  8 Comments
Mathieu NOE
Mathieu NOE on 15 Jul 2022
the saving in computation is proportionnal to the applied decimation factor (here 40) so I don't think it's negelctable especcially if you want to apply the code to longer wav files
but of course you can remove the decimation operation if you feel bad about it

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