# 6DoF SELD: Sound Event Localization and Detection Using Microphones and Motion Tracking Sensors on self-motioning human

arXiv: https://arxiv.org/abs/2403.01670 · HTML: https://mlsanity.com/paper/2403.01670/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2024.

## Built on by (most cited first, 3 of 3)

- [Adaptive Rotary Steering with Joint Autoregression for Robust Extraction of Closely Moving Speakers in Dynamic Scenarios](https://mlsanity.com/paper/2601.12345/) (2026)
- [Autoregressive Guidance of Deep Spatially Selective Filters using Bayesian Tracking for Efficient Extraction of Moving Speakers](https://mlsanity.com/paper/2603.23723/) (2026)
- [An Experimental Study on Joint Modeling for Sound Event Localization and Detection with Source Distance Estimation](https://mlsanity.com/paper/2501.10755/) (2025)

## Abstract (arXiv)

We aim to perform sound event localization and detection (SELD) using wearable equipment for a moving human, such as a pedestrian. Conventional SELD tasks have dealt only with microphone arrays located in static positions. However, self-motion with three rotational and three translational degrees of freedom (6DoF) shall be considered for wearable microphone arrays. A system trained only with a dataset using microphone arrays in a fixed position would be unable to adapt to the fast relative motion of sound events associated with self-motion, resulting in the degradation of SELD performance. To address this, we designed 6DoF SELD Dataset for wearable systems, the first SELD dataset considering the self-motion of microphones. Furthermore, we proposed a multi-modal SELD system that jointly utilizes audio and motion tracking sensor signals. These sensor signals are expected to help the system find useful acoustic cues for SELD on the basis of the current self-motion state. Experimental results on our dataset show that the proposed method effectively improves SELD performance with a mechanism to extract acoustic features conditioned by sensor signals.