A neural network trained microphone array system for noise reduction

This paper presents a neural network based microphone array system, which is capable to continuously perform speech enhancement and adaptation to nonuniform quantization, such as A-law and μ-law. Such a quantizer is designed to increase the signal to quantization noise ratio (SQNR) for small amplitudes in telecommunications systems. The proposed method primarily developed for hand-free mobile telephones, suppresses the ambient car noise with approximately 10 dB. The system is based upon a multilayer nonlinear backpropagation trained network by using a built-in calibration technique