Comparison of Learning-Based DOA Estimation Between SH Domain Features

Yonggang Hu, Sharon Gannot

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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 multimicrophone 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.

Original languageEnglish
Title of host publication30th European Signal Processing Conference, EUSIPCO 2022 - Proceedings
Number of pages5
ISBN (Electronic)9789082797091
StatePublished - 2022
Event30th European Signal Processing Conference, EUSIPCO 2022 - Belgrade, Serbia
Duration: 29 Aug 20222 Sep 2022

Publication series

NameEuropean Signal Processing Conference


Conference30th European Signal Processing Conference, EUSIPCO 2022


  • Learning-based direction-of-arrival estimation
  • relative harmonic coefficients
  • relative modal coherence

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering


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