  Polyphonic sound event detection and direction-of-arrival estimation require
different input features from audio signals. While sound event detection mainly
relies on time-frequency patterns, direction-of-arrival estimation relies on
magnitude or phase differences between microphones. Previous approaches use the
same input features for sound event detection and direction-of-arrival
estimation, and train the two tasks jointly or in a two-stage transfer-learning
manner. We propose a two-step approach that decouples the learning of the sound
event detection and directional-of-arrival estimation systems. In the first
step, we detect the sound events and estimate the directions-of-arrival
separately to optimize the performance of each system. In the second step, we
train a deep neural network to match the two output sequences of the event
detector and the direction-of-arrival estimator. This modular and hierarchical
approach allows the flexibility in the system design, and increase the
performance of the whole sound event localization and detection system. The
experimental results using the DCASE 2019 sound event localization and
detection dataset show an improved performance compared to the previous
state-of-the-art solutions.
