Binaural sound localization based on deep neural network and affinity propagation clustering in mismatched HRTF condition

Abstract

Binaural sound source localization is an important and widely used perceptually based method and it has been applied to machine learning studies by many researchers based on head-related transfer function (HRTF). Because the HRTF is closely related to human physiological structure, the HRTFs vary between individuals. Related machine learning studies to date tend to focus on binaural localization in reverberant or noisy environments, or in conditions with multiple simultaneously active sound sources. In contrast, mismatched HRTF condition, in which the HRTFs used to generate the training and test sets are different, is rarely studied. This mismatch leads to a degradation of localization performance. A basic solution to this problem is to introduce more data to improve generalization performance, which requires a lot. However, simply increasing the data volume will result in data-inefficiency. In this paper, we propose a data-efficient method based on deep neural network (DNN) and clustering to improve binaural localization performance in the mismatched HRTF condition. Firstly, we analyze the relationship between binaural cues and the sound source localization with a classification DNN. Different HRTFs are used to generate training and test sets, respectively. On this basis, we study the localization performance of DNN model trained by each training set on different test sets. The result shows that the localization performance of the same model on different test sets is different, while the localization performance of different models on the same test set may be similar. The result also shows a clustering trend. Secondly, different HRTFs are divided into several clusters. Finally, the corresponding HRTFs of each cluster center are selected to generate a new training set and to train a more generalized DNN model. The experimental results show that the proposed method achieves better generalization performance than the baseline methods in the mismatched HRTF condition and has almost equal performance to the DNN trained with a large number of HRTFs, which means the proposed method is data-efficient.