1 research outputs found
Multi-Source DOA Estimation through Pattern Recognition of the Modal Coherence of a Reverberant Soundfield
We propose a novel multi-source direction of arrival (DOA) estimation
technique using a convolutional neural network algorithm which learns the modal
coherence patterns of an incident soundfield through measured spherical
harmonic coefficients. We train our model for individual time-frequency bins in
the short-time Fourier transform spectrum by analyzing the unique snapshot of
modal coherence for each desired direction. The proposed method is capable of
estimating simultaneously active multiple sound sources on a D space using a
single-source training scheme. This single-source training scheme reduces the
training time and resource requirements as well as allows the reuse of the same
trained model for different multi-source combinations. The method is evaluated
against various simulated and practical noisy and reverberant environments with
varying acoustic criteria and found to outperform the baseline methods in terms
of DOA estimation accuracy. Furthermore, the proposed algorithm allows
independent training of azimuth and elevation during a full DOA estimation over
D space which significantly improves its training efficiency without
affecting the overall estimation accuracy