  A convolution neural network (CNN) based classification method for broadband
DOA estimation is proposed, where the phase component of the short-time Fourier
transform coefficients of the received microphone signals are directly fed into
the CNN and the features required for DOA estimation are learnt during
training. Since only the phase component of the input is used, the CNN can be
trained with synthesized noise signals, thereby making the preparation of the
training data set easier compared to using speech signals. Through experimental
evaluation, the ability of the proposed noise trained CNN framework to
generalize to speech sources is demonstrated. In addition, the robustness of
the system to noise, small perturbations in microphone positions, as well as
its ability to adapt to different acoustic conditions is investigated using
experiments with simulated and real data.
