基于深度学习的双耳声源定位算法研究

针对多种定位因素存在复杂关联且不易准确提取的问题，提出了以完整双耳声信号作为输入的、基于深度学习的双耳声源定位算法。首先，分别采用深层全连接后向传播神经网络(Deep Back Propagation Neural Network，D-BPNN)和卷积神经网络(Convolutional Neural Network， CNN)实现深度学习框架；然后，分别以水平面 15°、30°和 45°空间角度间隔的双耳声信号进行模型训练；最后，采用前后混乱率、定位准确率与训练时长等指标进行算法有效性分析。模型预测结果表明，CNN模型的前后混乱率远低于 D-BPNN；D-BPNN模型的定位准确率能够达到87%以上，而 CNN模型的定位准确率能够达到 98%左右；在相同实验条件下，CNN模型的训练时长大于 D-BPNN，且随着水平面角度间隔的减小，两者训练时长之间的差异愈发显著。

Binaural localization algorithm based on deep learning

Due to existence of complicated relationships between multiple localization cues, which causes them hard to be extracted accurately, a deep learning-based binaural sound source localization algorithm with complete binaural sound signals as input is proposed. Firstly, the deep fully connected back propagation neural network (D-BPNN) and the convolutional neural network (CNN) are used to implement the deep learning framework respectively. And then, binaural sound source signals with uniform azimuthal spacing of 15°, 30° and 45° in horizontal plane are applied to model training respectively. Finally, indicators such as front-back confusion rate, localization accuracy and training duration are used to investigate effectiveness of the models. The model prediction results show that the front-back confusion rate of the CNN model is much lower than that of D-BPNN model. The localization accuracy of the DBPNN model can reach more than 87%, while the localization accuracy of the CNN model is about 98%. Under the same experimental conditions, the training time of CNN model is longer than that of D-BPNN model; Moreover, this difference in training time becomes more and more obviously as the azimuthal spacing in the horizontal plane decreases.