In Search of Voice Activity Detection Algorithms - What happens to the frequency or pitch or speech features as speaker moves away from microphone Actually i am in  search of a Voice Activity Detection Algorithm which could distinguish between voice and non-voice

Roughly speaking it must not detect even a bullet sound,even a foot stepping and other non speech activity should only detect people conversation  or any one shouting

in that search this question arises in my mind and i want know the effect of noise and distance between speaker and microphone on the speech features like pitch,frequency,cepstrum,zerocrossing rate,power spectral density,entropy  etc

if some component wont get distorted i would like to extract that feature and do the activity decision on that 

Can any one help me in extracting dominant parameter of speech which would differentiate it from other common sounds even in Lower SNR conditions &lt;0dB

Note:my algorithm  expects voice activity happens at a distance of at least 10m away from microphone and continuous generator hum as background noise
The intrinsic fundamental frequency (pitch) of a speech signal do not change when the speaker moves away from the microphone (unless it moves away quite fast in which case it's a case of Doppler effect!) - it would be strange if we heard people's voice transposed up or down as they move away or as ambient noise increases!

However, as the distance from the microphone increases, the signal to noise ratio will increase, and this might affect the reliability of a naive pitch detector, so you might indeed get different result when estimating the f0 of a speech signal and the same signal with more noise. It doesn't mean that the f0 is different, just that the noise makes the estimate unreliable.

I am not aware of any naive feature which allows noise-robust speech/non-speech discrimination with a simple threshold rule. I think supervised machine learning would be the way to go for your problem.