  Our systems submitted to the DCASE2020 task~3: Sound Event Localization and
Detection (SELD) are described in this report. We consider two systems: a
single-stage system that solve sound event localization~(SEL) and sound event
detection~(SED) simultaneously, and a two-stage system that first handles the
SED and SEL tasks individually and later combines those results. As the
single-stage system, we propose a unified training framework that uses an
activity-coupled Cartesian DOA vector~(ACCDOA) representation as a single
target for both the SED and SEL tasks. To efficiently estimate sound event
locations and activities, we further propose RD3Net, which incorporates
recurrent and convolution layers with dense skip connections and dilation. To
generalize the models, we apply three data augmentation techniques: equalized
mixture data augmentation~(EMDA), rotation of first-order Ambisonic~(FOA)
singals, and multichannel extension of SpecAugment. Our systems demonstrate a
significant improvement over the baseline system.
