Microphone Array-Based Sound Source Localization Using Convolutional Residual Network

Abstract

Microphone array-based sound source localization (SSL) is widely used in a variety of occasions such as video conferencing, robotic hearing, speech enhancement, speech recognition and so on. The traditional SSL methods cannot achieve satisfactory performance in adverse noisy and reverberant environments. In order to improve localization performance, a novel SSL algorithm using convolutional residual network (CRN) is proposed in this paper. The spatial features including time difference of arrivals (TDOAs) between microphone pairs and steered response power-phase transform (SRP-PHAT) spatial spectrum are extracted in each Gammatone sub-band. The spatial features of different sub-bands with a frame are combine into a feature matrix as the input of CRN. The proposed algorithm employ CRN to fuse the spatial features. Since the CRN introduces the residual structure on the basis of the convolutional network, it reduce the difficulty of training procedure and accelerate the convergence of the model. A CRN model is learned from the training data in various reverberation and noise environments to establish the mapping regularity between the input feature and the sound azimuth. Through simulation verification, compared with the methods using traditional deep neural network, the proposed algorithm can achieve a better localization performance in SSL task, and provide better generalization capacity to untrained noise and reverberation.