An Efficient Real-time Voice Activity Detection Algorithm using Teager Energy to Energy Ratio
Mohammad Hadi, Mohammad Reza Pakravan, Mohammad Mahdi Razavi · 2019
We define a new feature called Teager Energy to Energy and mathematically show that it provides distinguished values for pure tone and white noise signals. We then employ the Teager Energy to Energy feature to propose an efficient procedure for voice activity detection and use simulation results to evaluate its performance in different noisy environments. Furthermore, we experimentally demonstrate the performance of the proposed voice activity detection technique in a real-time voice processing embedded system. Experimental and simulation results show that the introduced procedure provides more reliable results with a reasonable amount of computational complexity in comparison with its conventional counterparts and can extract voice frames from a -5 dB SNR noisy voice signal with a success probability of 89.4%.