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Comparison of Learning-Based DOA Estimation Between SH Domain Features

Accurate direction-of-arrival (DOA) estimation in noisy and reverberant environments is a long-standing challenge in the field of acoustic signal processing. One of the promising research directions utilizes the decomposition of the multi-microphone measurements into the spherical harmonics (SH) domain. This paper presents an evaluation and comparison of learning-based single-source DOA estimation using two recently introduced SH domain features denoted relative harmonic coefficients (RHC) and relative modal coherence (RMC), respectively. Both features were shown to be independent of the time-varying source signal even in reverberant environments, thus facilitating training with synthesized, continuously-active, noise signal rather than with speech signal. The inspected features are fed into a convolutional neural network, trained as a DOA classifier. Extensive validations confirm that the RHC-based method outperforms the RMC-based method, especially under unfavorable scenarios with severe noise and reverberation.