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Recent neural network based Direction of Arrival (DoA) estimation algorithmshave performed well on unknown number of sound sources scenarios. Thesealgorithms are usually achieved by mapping the multi-channel audio input to thesingle output (i.e. overall spatial pseudo-spectrum (SPS) of all sources), thatis called MISO. However, such MISO algorithms strongly depend on empiricalthreshold setting and the angle assumption that the angles between the soundsources are greater than a fixed angle. To address these limitations, wepropose a novel multi-channel input and multiple outputs DoA network calledMIMO-DoAnet. Unlike the general MISO algorithms, MIMO-DoAnet predicts the SPScoding of each sound source with the help of the informative spatial covariancematrix. By doing so, the threshold task of detecting the number of soundsources becomes an easier task of detecting whether there is a sound source ineach output, and the serious interaction between sound sources disappearsduring inference stage. Experimental results show that MIMO-DoAnet achievesrelative 18.6% and absolute 13.3%, relative 34.4% and absolute 20.2% F1 scoreimprovement compared with the MISO baseline system in 3, 4 sources scenes. Theresults also demonstrate MIMO-DoAnet alleviates the threshold setting problemand solves the angle assumption problem effectively.

MIMO-DoAnet: Multi-channel Input and Multiple Outputs DoA Network with Unknown Number of Sound Sources