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We propose a direction-of-arrival (DOA) estimation method for Sound EventLocalization and Detection (SELD). Direct estimation of DOA using a deep neuralnetwork (DNN), i.e. completely-datadriven approach, achieves high accuracy.However, there is a gap in the accuracy between DOA estimation for single andoverlapping sources because they cannot incorporate physical knowledge.Meanwhile, although the accuracy of physics-based approaches is inferior toDNN-based approaches, it is robust for overlapping source. In this study, weconsider a combination of physics-based and DNN-based approaches; the soundintensity vectors (IVs) for physics-based DOA estimation is refined based onDNN-based denoising and source separation. This method enables the accurate DOAestimation for both single and overlapping sources using a spherical microphonearray. Experimental results show that the proposed method achievesstate-of-the-art DOA estimation accuracy on an open dataset of the SELD.

Sound Event Localization based on Sound Intensity Vector Refined By DNN-Based Denoising and Source Separation