In order to solve the problem of insufficient hit rates of voice activity detection (VAD) in complex background noise environments, an environment-aware VAD algorithm is proposed. Aiming at the poor noise immunity of the single fixed threshold method used in conventional algorithms, different thresholds are adopted during the mutual conversion processes of voice and noise frames, and the thresholds are updated adaptively. And a method of feature combination is proposed to overcome the defect that a single feature cannot cope with the complex noise environments, which combines the likelihood ratio, energy entropy characteristic, and mean harmonic number value characteristic. Then, the idea of environmental noise classification is introduced, which classifies the noise environments using supported vector machine and selects optimal feature combination according to the type of noise environments, so as to improve the performance of the algorithm further. Finally, simulation experiments are conducted to evaluate the performance of the proposed algorithm, in which the NOIZEUS speech database is utilized, and five kinds of noises such as babble, pink, white, f16 and volvo are selected as background noise. And the hit rates of various feature combinations are compared to verify the effectiveness of the algorithm. Experimental results show that the proposed algorithm outperforms existing algorithms and can achieve about 80 % overall hit rate in various noise environments, and it can balance the voice hit rate and the false alarm rate as well.
