I'm trying to get a qualitative handle on the amount of static or noise present in a audio stream. The normal content of the stream is voice or music.
I've been experiementing with taking the stddev of the samples, and that does give me some handle on the presence of voice vs. empty channel noise (ie. a high stddev usually indicates voice or music)
Was wondering if anyone else had some pointers on this.
Doesn't the peak value give you the answer? If you're looking at a signal from a good ADC, the ambient level should be in the 1's or 10's of counts, while voice or music will get up into the thousands of counts. Is there some kind of automatic gain control that makes this strategy not work?
If you need something more complex, the peak to RMS ratio might be a bit more reliable than simply RMS level (RMS = stddev). Pure noise will have a ratio of around 3-5, while sinusoids, for instance, have a peak to RMS ratio of 1.4. However, you can get more discrimination by looking at the spectrum of the signal. Static is usually spectrally smooth or even flat, while voice and music are spectrally structured. So a Fourier transform might be what you're looking for. Assuming a signal x that contains, say 0.5 seconds worth of data, here's some Matlab code:
Sx = fft(x .* hann(length(x), 'periodic'))
The HANN function applies a Hann window to reduce spectral leakage, while the FFT function quickly calculates the Fourier transform. Now you have a couple of choices. If you want to determine whether the signal x consists of static or voice/music, take the peak to RMS ratio of the spectrum:
pk2rms = max(abs(Sx))/sqrt(sum(abs(Sx).^2)/length(Sx))
I'd expect pure static to have a peak to RMS ratio around 3-5 (again), while voice/music would be at least an order of magnitude higher. This takes advantage of the fact that pure white noise has the same "structure" in time and frequency domains.
If you want to get a numerical estimate of the noise level, you can calculate the power in Sx over time, using an average:
Gxx = ((k-1)*Gxx + Sx.*conj(Sx))/k
Over time, the peaks in Gxx should come and go, but you should see a constant minimum value corresponding to the noise floor. In general, audio spectra are easier to look at on a dB (log vertical) scale.
Some notes:
1. I picked 0.5 seconds for the length of x, but I'm not sure what an optimal value here is. If you pick a value that's too short, x will not have much structure. In that case, the DC component of the signal will have a lot of energy. I expect you can still use the peak to RMS discriminator, though, if you first toss out the bin in Sx corresponding to DC.
2. I'm not sure what a good value for k is, but that equation corresponds to exponential averaging. You can probably experiment with k to figure out an optimal value. This might work best with a short x.
There are different kinds of noise. White, pink, brown. Noise can come from many places. Is a 60hertz hum noise or signal?
For white noise, I'd look at the fft and find the lowest value to see what your noise floor is.