# Polyphonic Sound Event Detection and Localization using a Two-Stage Strategy

## TL;DR

This paper introduces a two-stage method for polyphonic sound event detection and localization that improves performance by sequentially training SED and DOA estimation, leveraging transfer learning and masking techniques.

## Contribution

It proposes a novel two-stage approach that enhances both sound event detection and localization by separating training phases and using ground truth masks.

## Key findings

- Improved SED and DOA estimation performance.
- Significant outperforming of baseline methods.
- Effective transfer of learned features between tasks.

## Abstract

Sound event detection (SED) and localization refer to recognizing sound events and estimating their spatial and temporal locations. Using neural networks has become the prevailing method for SED. In the area of sound localization, which is usually performed by estimating the direction of arrival (DOA), learning-based methods have recently been developed. In this paper, it is experimentally shown that the trained SED model is able to contribute to the direction of arrival estimation (DOAE). However, joint training of SED and DOAE degrades the performance of both. Based on these results, a two-stage polyphonic sound event detection and localization method is proposed. The method learns SED first, after which the learned feature layers are transferred for DOAE. It then uses the SED ground truth as a mask to train DOAE. The proposed method is evaluated on the DCASE 2019 Task 3 dataset, which contains different overlapping sound events in different environments. Experimental results show that the proposed method is able to improve the performance of both SED and DOAE, and also performs significantly better than the baseline method.