Deep neural network based direction of arrival estimation for hearing aid applications using smartphone

Deep neural network (DNN) techniques are gaining popularity due to performance boost in many applications. In this work we propose a DNN-based method for finding the direction of arrival (DOA) of speech source for hearing aid applications using smartphones. We consider the DOA estimation as a classification problem and use the magnitude and phase of speech signal as a feature set for DNN training stage and obtaining appropriate model. The model is trained and derived using real noisy speech data recorded on smartphone in different environments under low SNRs. The DNN-based DOA method, with the pre-trained model, is implemented and run on Android smartphone in real time and evaluated objectively and subjectively. The test results are presented showing the performance of proposed method versus other methods.