Synergistic Optimization based Binaural TimeFrequency Masking for Speech Source Localization Monaural timefrequency (TF) masking has been demonstrated to advance the performance of binaural speech source localization. However, it fails to consider interaural information, which may result in severe distortion of interaural cues. To mitigate these impacts, this paper presents a novel method for binaural speech source localization based on binaural TF masking. Firstly, the CNNbased binaural TF masking network is designed to suppress the noise and reverberation in TF fragments, which is trained in the independent stage. Then, the resulted binaural TF masking is synergistically refined with the localization network to compensate for the distorted interaural cues. The final source direction is estimated using the trained network. The proposed method is compared with other baseline methods and twostage models composed by cascade TF masking network and localization network. Experimental results show our method outperforms the other compared methods in the adverse environments with different reverberation time and signaltonoise ratios.

Robust Interaural Time Difference Estimation Based on Convolutional Neural Network This paper proposes a novel cross correlation function (CCF) extraction method based on convolutional neural network for time difference of arrival (TDOA) estimation or further direction of arrival (DOA) estimation. CNN is utilized to learn the relationship between the cross correlation localization features and the preprocessed waveform signal which may include not only the source signal but also the background noise and reverberation. In contrast to many previous sound source localization approaches, the proposed method focuses on the spatial feature extraction. Two kind of outputs, grouped or encoded CCF, are designed to capture the implicit tendency of location information. The experimental results demonstrate that the proposed method outperforms the conventional TDOA estimation methods under environments with different levels of noise and reverberation.