Distant Noise Reduction Based on Multi-delay Noise Model Using Distributed Microphone Array

We propose a novel framework for reducing distant noise by using a distributed microphone array; reducing noise propagated from a far distance in real-time. Previous studies have revealed that a distributed microphone array with an instantaneous mixing assumption can effectively reduce noise when the target and noise sources are significantly far apart. However, in distant noise reduction, the target and noise sources are not usually instantaneously mixed because the reverberation-and propagation-time from the noise sources to a microphone is longer than the short-time Fourier transform (STFT) length. To express reverberation-and propagation-parameters, we introduce a multi-delay noise model that represents the reverberation-time as a convolution of the transfer-function-gains and the noise sources and the propagation-time as time-frame delays. These parameters are estimated on the basis of the maximum a posteriori (MAP) estimation. Experimental results show that the proposed method outperformed conventional methods in several performance measurements and could reduce distant noise propagated from more than 100 m away in a real-environment.