Abstract: A two-stage supervised binaural distance estimation,algorithm in room is proposed to overcome the adverse impacts of the change of azimuth angles on the distance estimation performance.At the first stage,the algorithm estimates the azimuth of binaural signals using Deep Neural Networks(DNN) models and classify the binaural signals based on the estimated azimuth.At the second stage,for the binaural signals of each azimuth angle,the distance is estimated by exploiting azimuth-dependent DNN models,in which some binaural features and statistical properties are selected as distance features.In simulated and real acoustic environments,the distance estimation accuracy of the proposed algorithm is about 3%-5% and 5%-10%higher than the existing algorithms in matched and mismatched conditions,respectively.Experimental results demonstrate that the distance estimation performance of binaural distance estimation methods can be effectively improved by utilizing the azimuthal information.