# A multi-room reverberant dataset for sound event localization and detection

## TL;DR

This paper introduces a comprehensive multi-room reverberant dataset for sound event localization and detection, along with a baseline neural network method to benchmark performance in complex acoustic environments.

## Contribution

It provides a novel synthesized dataset with spatialized sound events in multiple rooms and establishes baseline scores for the SELD task in reverberant conditions.

## Key findings

- Baseline neural network achieves measurable SELD performance.
- Dataset captures diverse room acoustics with real impulse responses.
- Benchmark scores facilitate future research comparisons.

## Abstract

This paper presents the sound event localization and detection (SELD) task setup for the DCASE 2019 challenge. The goal of the SELD task is to detect the temporal activities of a known set of sound event classes, and further localize them in space when active. As part of the challenge, a synthesized dataset with each sound event associated with a spatial coordinate represented using azimuth and elevation angles is provided. These sound events are spatialized using real-life impulse responses collected at multiple spatial coordinates in five different rooms with varying dimensions and material properties. A baseline SELD method employing a convolutional recurrent neural network is used to generate benchmark scores for this reverberant dataset. The benchmark scores are obtained using the recommended cross-validation setup.