  Supervised learning based methods for source localization, being data driven,
can be adapted to different acoustic conditions via training and have been
shown to be robust to adverse acoustic environments. In this paper, a
convolutional neural network (CNN) based supervised learning method for
estimating the direction-of-arrival (DOA) of multiple speakers is proposed.
Multi-speaker DOA estimation is formulated as a multi-class multi-label
classification problem, where the assignment of each DOA label to the input
feature is treated as a separate binary classification problem. The phase
component of the short-time Fourier transform (STFT) coefficients of the
received microphone signals are directly fed into the CNN, and the features for
DOA estimation are learnt during training. Utilizing the assumption of disjoint
speaker activity in the STFT domain, a novel method is proposed to train the
CNN with synthesized noise signals. Through experimental evaluation with both
simulated and measured acoustic impulse responses, the ability of the proposed
DOA estimation approach to adapt to unseen acoustic conditions and its
robustness to unseen noise type is demonstrated. Through additional empirical
investigation, it is also shown that with an array of M microphones our
proposed framework yields the best localization performance with M-1
convolution layers. The ability of the proposed method to accurately localize
speakers in a dynamic acoustic scenario with varying number of sources is also
shown.
