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Polyphonic sound event detection and localization (SELD) task is challengingbecause it is difficult to jointly optimize sound event detection (SED) anddirection-of-arrival (DOA) estimation in the same network. We propose a generalnetwork architecture for SELD in which the SELD network comprises sub-networksthat are pretrained to solve SED and DOA estimation independently, and arecurrent layer that combines the SED and DOA estimation outputs into SELDoutputs. The recurrent layer does the alignment between the sound classes andDOAs of sound events while being unaware of how these outputs are produced bythe upstream SED and DOA estimation algorithms. This simple networkarchitecture is compatible with different existing SED and DOA estimationalgorithms. It is highly practical since the sub-networks can be improvedindependently. The experimental results using the DCASE 2020 SELD dataset showthat the performances of our proposed network architecture using different SEDand DOA estimation algorithms and different audio formats are competitive withother state-of-the-art SELD algorithms. The source code for the proposed SELDnetwork architecture is available at Github.

A General Network Architecture for Sound Event Localization and Detection Using Transfer Learning and Recurrent Neural Network