The invention discloses a
voice activity detection method in a communication-terminal double-
microphone denoising
system and an apparatus thereof. The method is characterized by firstly, training a neural network: selecting a training sample, extracting a characteristic and acquiring a corresponding
voice activity detection result; using the characteristic and the corresponding detection result to
train the neural network; secondly, based on the trained neural network, carrying out
voice activity detection: using primary and secondary microphones of a communication terminal respectively to collect voice signals with noises to be detected; extracting a characteristic from the collected voice signals with noises, sending the characteristic to the trained neural network and using the neural network to output a
voice activity detection result. The characteristic comprises that a sub-band mutual channel energy difference is related to a normalized mutual channel. According to different
noise environments, a parameter is adaptively adjusted and the
voice activity detection is performed. A problem that an existing
voice activity detection method can not adapt to changes of a
noise environment so that performance is decreased is solved.
Voice activity detection accuracy under a complex
noise environment is increased.