2 papers with code • 1 benchmarks • 1 datasets
Estimating the direction-of-arrival (DOA) of a sound source from multi-channel recordings.
We present a novel learning-based approach to estimate the direction-of-arrival (DOA) of a sound source using a convolutional recurrent neural network (CRNN) trained via regression on synthetic data and Cartesian labels.
Ranked #1 on Direction of Arrival Estimation on SOFA (using extra training data)
We propose a generalized formulation of direction of arrival estimation that includes many existing methods such as steered response power, subspace, coherent and incoherent, as well as speech sparsity-based methods.