在室内麦克风阵列声源定位算法的研究中,混响和噪声对定位精度影响很大,传统的声源定位算法无法在高混响和低信噪比的环境中保持较高的定位精度。为了解决这一问题,提出一种基于卷积神经网络的室内声源定位算法,该算法提取麦克风阵列接收信号的相位加权广义互相关函数作为训练特征,获取目标声源三维位置信息。基于NOIZEUS数据库的实验结果表明,该方法能够通过训练适应不同的声学环境,与其他基于学习的室内声源定位算法相比,其在高混响与低信噪比环境下仍具有较好的定位性能与鲁棒性,具有较大的研究和应用价值。

For indoor sound source localization algorithm based on microphone arrays, its accuracy is greatly influenced by the reverberation and noise. Traditional sound source localization approaches cannot keep high localization accuracy in strong reverberation and low signal-to-noise ratio environments. To tackle this problem, a novel indoor sound source localization algorithm based on convolutional neural network is proposed. By extracting the phase weighted generalized cross correlation function of the received signals from microphone arrays as training feature, the three-dimensional localization information of target sound source can be obtained. Experiments based on NOIZEUS database demonstrate that the proposed algorithm can be adapted to different acoustic conditions via training. Compared with other learning based indoor sound source localization algorithms, the proposed algorithm has good localization performance and robustness in strong reverberation and low signal-to-noise ratio environment, suggesting high research and application value.