Direction of arrival estimation for multiple sound sources using convolutional recurrent neural network
This work addresses direction of arrival estimation for sound sources, which is important for applications like audio processing and robotics, but it appears incremental as it builds on existing neural network approaches.
The paper tackled the problem of estimating directions of arrival for multiple sound sources by proposing DOAnet, a deep neural network that uses spectrogram magnitudes and phases as input, and it achieved good precision in estimating source numbers and DOAs with high signal-to-noise ratio in various acoustic conditions.
This paper proposes a deep neural network for estimating the directions of arrival (DOA) of multiple sound sources. The proposed stacked convolutional and recurrent neural network (DOAnet) generates a spatial pseudo-spectrum (SPS) along with the DOA estimates in both azimuth and elevation. We avoid any explicit feature extraction step by using the magnitudes and phases of the spectrograms of all the channels as input to the network. The proposed DOAnet is evaluated by estimating the DOAs of multiple concurrently present sources in anechoic, matched and unmatched reverberant conditions. The results show that the proposed DOAnet is capable of estimating the number of sources and their respective DOAs with good precision and generate SPS with high signal-to-noise ratio.