  Deep neural networks (DNNs) have greatly benefited direction of arrival (DoA)
estimation methods for speech source localization in noisy environments.
However, their localization accuracy is still far from satisfactory due to the
vulnerability to nonspeech interference. To improve the robustness against
interference, we propose a DNN based normalized time-frequency (T-F) weighted
criterion which minimizes the distance between the candidate steering vectors
and the filtered snapshots in the T-F domain. Our method requires no
eigendecomposition and uses a simple normalization to prevent the optimization
objective from being misled by noisy filtered snapshots. We also study
different designs of T-F weights guided by a DNN. We find that duplicating the
Hadamard product of speech ratio masks is highly effective and better than
other techniques such as direct masking and taking the mean in the proposed
approach. However, the best-performing design of T-F weights is
criterion-dependent in general. Experiments show that the proposed method
outperforms popular DNN based DoA estimation methods including widely used
subspace methods in noisy and reverberant environments.
