Joint estimation of binaural distance and azimuth by exploiting deep neural networks

The state-of-the-art supervised binaural distance estimation methods often use binaural features that are related to both the distance and the azimuth, and thus the distance estimation accuracy may degrade a great deal with fluctuant azimuth. To incorporate the azimuth on estimating the distance, this paper proposes a supervised method to jointly estimate the azimuth and the distance of binaural signals based on deep neural networks (DNNs). In this method, the subband binaural features, including many statistical properties of several subband binaural features and the binaural spectral magnitude difference standard deviation, are extracted together as cues to jointly estimate the azimuth and the distance using binaural signals by exploiting a multi-objective DNN framework. Especially, both the azimuth and the distance cues are utilized in the learning stage of the error back-propagation in the multi-objective DNN framework, which can improve the generalization ability of the azimuth and the distance estimation. Experimental results demonstrate that the proposed method can not only achieve high azimuth estimation accuracy but can also effectively improve the distance estimation accuracy when compared with several state-of-the-art supervised binaural distance estimation methods.