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.