Multi-Source DOA Estimation through Pattern Recognition of the Modal Coherence of a Reverberant Soundfield
This addresses DOA estimation for acoustic signal processing in noisy, reverberant settings, offering an incremental improvement with efficiency gains.
The paper tackles multi-source direction of arrival (DOA) estimation in 3D space by proposing a convolutional neural network that learns modal coherence patterns from spherical harmonic coefficients, using a single-source training scheme to reduce resource needs and allow model reuse. It outperforms baseline methods in accuracy across simulated and practical noisy, reverberant environments.
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 $3$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 $3$D space which significantly improves its training efficiency without affecting the overall estimation accuracy.