# An Experimental Study on Joint Modeling for Sound Event Localization and Detection with Source Distance Estimation

arXiv: https://arxiv.org/abs/2501.10755 · HTML: https://mlsanity.com/paper/2501.10755/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 33 references that are in the map.
- First posted on arXiv 18 Jan 2025.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [FSD50K: An Open Dataset of Human-Labeled Sound Events](https://mlsanity.com/paper/2010.00475/) (2020)
- [Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks](https://mlsanity.com/paper/1807.00129/) (2018)
- [STARSS23: An Audio-Visual Dataset of Spatial Recordings of Real Scenes with Spatiotemporal Annotations of Sound Events](https://mlsanity.com/paper/2306.09126/) (2023)
- [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)
- [Multi-ACCDOA: Localizing and Detecting Overlapping Sounds from the Same Class with Auxiliary Duplicating Permutation Invariant Training](https://mlsanity.com/paper/2110.07124/) (2021)
- [A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection](https://mlsanity.com/paper/2006.01919/) (2020)
- [A Four-Stage Data Augmentation Approach to ResNet-Conformer Based Acoustic Modeling for Sound Event Localization and Detection](https://mlsanity.com/paper/2101.02919/) (2021)
- [Sound Event Detection and Localization with Distance Estimation](https://mlsanity.com/paper/2403.11827/) (2024)
- [Spatial Scaper: A Library to Simulate and Augment Soundscapes for Sound Event Localization and Detection in Realistic Rooms](https://mlsanity.com/paper/2401.12238/) (2024)
- [Event-Independent Network for Polyphonic Sound Event Localization and Detection](https://mlsanity.com/paper/2010.00140/) (2020)
- [Sound Source Distance Estimation in Diverse and Dynamic Acoustic Conditions](https://mlsanity.com/paper/2309.09288/) (2023)
- [CST-former: Transformer with Channel-Spectro-Temporal Attention for Sound Event Localization and Detection](https://mlsanity.com/paper/2312.12821/) (2023)
- [A Track-Wise Ensemble Event Independent Network for Polyphonic Sound Event Localization and Detection](https://mlsanity.com/paper/2203.10228/) (2022)
- [Exploring Audio-Visual Information Fusion for Sound Event Localization and Detection In Low-Resource Realistic Scenarios](https://mlsanity.com/paper/2406.15160/) (2024)
- [AD-YOLO: You Look Only Once in Training Multiple Sound Event Localization and Detection](https://mlsanity.com/paper/2303.15703/) (2023)
- [6DoF SELD: Sound Event Localization and Detection Using Microphones and Motion Tracking Sensors on self-motioning human](https://mlsanity.com/paper/2403.01670/) (2024)
- [Squeeze-and-Excite ResNet-Conformers for Sound Event Localization, Detection, and Distance Estimation for DCASE 2024 Challenge](https://mlsanity.com/paper/2407.09021/) (2024)

## Abstract (arXiv)

In traditional sound event localization and detection (SELD) tasks, the focus is typically on sound event detection (SED) and direction-of-arrival (DOA) estimation, but they fall short of providing full spatial information about the sound source. The 3D SELD task addresses this limitation by integrating source distance estimation (SDE), allowing for complete spatial localization. We propose three approaches to tackle this challenge: a novel method with independent training and joint prediction, which firstly treats DOA and distance estimation as separate tasks and then combines them to solve 3D SELD; a dual-branch representation with source Cartesian coordinate used for simultaneous DOA and distance estimation; and a three-branch structure that jointly models SED, DOA, and SDE within a unified framework. Our proposed method ranked first in the DCASE 2024 Challenge Task 3, demonstrating the effectiveness of joint modeling for addressing the 3D SELD task. The relevant code for this paper will be open-sourced in the future.