# What Makes Sound Event Localization and Detection Difficult? Insights from Error Analysis

arXiv: https://arxiv.org/abs/2107.10469 · HTML: https://mlsanity.com/paper/2107.10469/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 26 references that are in the map.
- First posted on arXiv 22 Jul 2021.

## Builds on (most cited first, 19 of 19)

- [Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification](https://mlsanity.com/paper/1608.04363/) (2016)
- [Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks](https://mlsanity.com/paper/1807.00129/) (2018)
- [Overview and Evaluation of Sound Event Localization and Detection in DCASE 2019](https://mlsanity.com/paper/2009.02792/) (2020)
- [Direction of arrival estimation for multiple sound sources using convolutional recurrent neural network](https://mlsanity.com/paper/1710.10059/) (2017)
- [ACCDOA: Activity-Coupled Cartesian Direction of Arrival Representation for Sound Event Localization and Detection](https://mlsanity.com/paper/2010.15306/) (2020)
- [An Improved Event-Independent Network for Polyphonic Sound Event Localization and Detection](https://mlsanity.com/paper/2010.13092/) (2020)
- [A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection](https://mlsanity.com/paper/2006.01919/) (2020)
- [Densely connected multidilated convolutional networks for dense prediction tasks](https://mlsanity.com/paper/2011.11844/) (2020)
- [A Dataset of Dynamic Reverberant Sound Scenes with Directional Interferers for Sound Event Localization and Detection](https://mlsanity.com/paper/2106.06999/) (2021)
- [Polyphonic Sound Event Detection and Localization using a Two-Stage Strategy](https://mlsanity.com/paper/1905.00268/) (2019)
- [A multi-room reverberant dataset for sound event localization and detection](https://mlsanity.com/paper/1905.08546/) (2019)
- [A Sequence Matching Network for Polyphonic Sound Event Localization and Detection](https://mlsanity.com/paper/2002.05865/) (2020)
- [The NIGENS General Sound Events Database](https://mlsanity.com/paper/1902.08314/) (2019)
- [Sound source detection, localization and classification using consecutive ensemble of CRNN models](https://mlsanity.com/paper/1908.00766/) (2019)
- [A General Network Architecture for Sound Event Localization and Detection Using Transfer Learning and Recurrent Neural Network](https://mlsanity.com/paper/2011.07859/) (2020)
- [Ensemble of ACCDOA- and EINV2-based Systems with D3Nets and Impulse Response Simulation for Sound Event Localization and Detection](https://mlsanity.com/paper/2106.10806/) (2021)
- [Localization of Simultaneous Moving Sound Sources for Mobile Robot Using a Frequency-Domain Steered Beamformer Approach](https://mlsanity.com/paper/1602.08629/) (2016)
- [Bird detection in audio: a survey and a challenge](https://mlsanity.com/paper/1608.03417/) (2016)
- [DCASE 2021 Task 3: Spectrotemporally-aligned Features for Polyphonic Sound Event Localization and Detection](https://mlsanity.com/paper/2106.15190/) (2021)

## Abstract (arXiv)

Sound event localization and detection (SELD) is an emerging research topic that aims to unify the tasks of sound event detection and direction-of-arrival estimation. As a result, SELD inherits the challenges of both tasks, such as noise, reverberation, interference, polyphony, and non-stationarity of sound sources. Furthermore, SELD often faces an additional challenge of assigning correct correspondences between the detected sound classes and directions of arrival to multiple overlapping sound events. Previous studies have shown that unknown interferences in reverberant environments often cause major degradation in the performance of SELD systems. To further understand the challenges of the SELD task, we performed a detailed error analysis on two of our SELD systems, which both ranked second in the team category of DCASE SELD Challenge, one in 2020 and one in 2021. Experimental results indicate polyphony as the main challenge in SELD, due to the difficulty in detecting all sound events of interest. In addition, the SELD systems tend to make fewer errors for the polyphonic scenario that is dominant in the training set.