  Performance of sound event localization and detection (SELD) in real scenes
is limited by small size of SELD dataset, due to difficulty in obtaining
sufficient amount of realistic multi-channel audio data recordings with
accurate label. We used two main strategies to solve problems arising from the
small real SELD dataset. First, we applied various data augmentation methods on
all data dimensions: channel, frequency and time. We also propose original data
augmentation method named Moderate Mixup in order to simulate situations where
noise floor or interfering events exist. Second, we applied
Squeeze-and-Excitation block on channel and frequency dimensions to efficiently
extract feature characteristics. Result of our trained models on the STARSS22
test dataset achieved the best ER, F1, LE, and LR of 0.53, 49.8%, 16.0deg., and
56.2% respectively.
