# Ensemble of ACCDOA- and EINV2-based Systems with D3Nets and Impulse Response Simulation for Sound Event Localization and Detection

arXiv: https://arxiv.org/abs/2106.10806 · HTML: https://mlsanity.com/paper/2106.10806/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2021.

## Built on by (most cited first, 5 of 5)

- [A Survey of Sound Source Localization with Deep Learning Methods](https://mlsanity.com/paper/2109.03465/) (2021)
- [End-to-End Complex-Valued Multidilated Convolutional Neural Network for Joint Acoustic Echo Cancellation and Noise Suppression](https://mlsanity.com/paper/2110.00745/) (2021)
- [Locate This, Not That: Class-Conditioned Sound Event DOA Estimation](https://mlsanity.com/paper/2203.04197/) (2022)
- [What Makes Sound Event Localization and Detection Difficult? Insights from Error Analysis](https://mlsanity.com/paper/2107.10469/) (2021)
- [Spatial mixup: Directional loudness modification as data augmentation for sound event localization and detection](https://mlsanity.com/paper/2110.06126/) (2021)

## Abstract (arXiv)

This report describes our systems submitted to the DCASE2021 challenge task 3: sound event localization and detection (SELD) with directional interference. Our previous system based on activity-coupled Cartesian direction of arrival (ACCDOA) representation enables us to solve a SELD task with a single target. This ACCDOA-based system with efficient network architecture called RD3Net and data augmentation techniques outperformed state-of-the-art SELD systems in terms of localization and location-dependent detection. Using the ACCDOA-based system as a base, we perform model ensembles by averaging outputs of several systems trained with different conditions such as input features, training folds, and model architectures. We also use the event independent network v2 (EINV2)-based system to increase the diversity of the model ensembles. To generalize the models, we further propose impulse response simulation (IRS), which generates simulated multi-channel signals by convolving simulated room impulse responses (RIRs) with source signals extracted from the original dataset. Our systems significantly improved over the baseline system on the development dataset.