DOA Estimation by DNN-based Denoising and Dereverberation from Sound Intensity Vector

DOA ESTIMA TION BY DNN-BASED DENOISING AND DEREVERBERA TION FROM SOUND INTENSITY VECTOR Masahiro Y asuda 1, Y uma Koizumi 1, Luca Mazzon 2, Shoichiro Saito 1 and Hisashi Uematsu 1 1 NTT Media Intelligence Laboratories, Tokyo, Japan 2 University of Padova, Padua, Italy ABSTRACT We propose a direction of arrival (DOA) estimation method that combines sound-intensity vector (IV)-based DOA estimation and DNN-based denoising and dereverberation. Since the accuracy of IV -based DOA estimation degrades due to environmental noise and reverberation, two DNNs are used to remove such effects from the observed IVs. DOA is then estimated from the refined IVs based on the physics of wave propagation. Experiments on an open dataset showed that the average DOA error of the proposed method was 0.528 degrees, and it outperformed a conventional IV -based and DNN-based DOA estimation method. Index T erms-- direction of arrival, deep neural network, sound intensity vector, sound activity detection 1. INTRODUCTION Time series direction-of-arrival (DOA) estimation, which is the task of identifying the relative position of the sound sources with respect to the microphone at every time frame, is an important technology for understanding the surrounding environment from sound recordings. For example, DOA estimation is useful for autonomous driving that autonomously acquiring the surrounding environment [1].