matlab - I want to add more noise to noisy audio, why is my calculation test for signal to noise not correct -


i have nothing audio recording want lower signal noise of. simplicity, extracted specific part of audio want measure signal noise, called audio. have extracted background noise area has no signal in it, called backgroundnoise data here: http://expirebox.com/download/bb9997de2dc5bae12fc7184dd2a0eb0f.html

due lot of here: proper way add noise signal

i have :

originalsnr=snr(audio-backgroundnoise,backgroundnoise) %initial calculation 6.4762 desiredsnrindb = 5:-1:1;    %what want lower snr down desired_snr_db=desiredsnrindb     est_signal= audio- backgroundnoise;      %the original matlab equation snr is:     %signalpow = rssq(x(:)).^2;     %noisepow = rssq(y(:)).^2;     %r = 10 * log10(signalpow / noisepow);     %solving noisepow     rms_new = rssq(est_signal(:)).^2 / 10^(desired_snr_db / 10);      %assuming rssq(new_noise).^2 = scale_factor * rssq(old).^2     scale_factor = rms_new / rssq(backgroundnoise(:)).^2;      new_noise = scale_factor * backgroundnoise;      %add noise our estimated signal     new_signal = est_signal + new_noise; %you may need stretching     %trimming right sizes      snr(new_signal-backgroundnoise,backgroundnoise)  end

but problem

  1. my test snr @ end not same desired_snr_db, ,
  2. the snr goes if desired_snr_db low.

can help?

an obvious issue lines:

scale_factor = rms_new / rssq(backgroundnoise(:)).^2; new_noise = scale_factor * backgroundnoise;

more specifically, first line computes scaling factor applied on background noise power, whereas second line applies background noise samples. scale noise power scale_factor, you'd need scale samples sqrt(scale_factor) as:

scale_factor = sqrt(rms_new / rssq(backgroundnoise(:)).^2); new_noise = scale_factor * backgroundnoise;

another problem data sample provide how handle background noise in first place. direct subtraction of noise segment noisy signal in fact adding noise, unless subtracted noise estimate vector has been obtained correlated actual noise on sample-by-sample basis (and not appear case sample). subtracting typical noise sample similar characteristics (such noise power) not enough remove noise.

in general noise can reduced using 1 of various noise cancellation techniques. these typically involves filtering noisy signal either adaptive filters or fixed filters (less complex requires a-priori knowledge of signal , noise characteristics).


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