# w2v-SELD: A Sound Event Localization and Detection Framework for Self-Supervised Spatial Audio Pre-Training

arXiv: https://arxiv.org/abs/2312.06907 · HTML: https://mlsanity.com/paper/2312.06907/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2023.

## Built on by (most cited first, 2 of 2)

- [Acoustic Imaging for UAV Detection: Dense Beamformed Energy Maps and U-Net SELD](https://mlsanity.com/paper/2508.00307/) (2025)
- [Bearings: Self-Supervised Soundfield Embeddings from First-Order Ambisonics](https://mlsanity.com/paper/2609.23152/) (2026)

## Abstract (arXiv)

Sound Event Detection and Localization (SELD) constitutes a complex task that depends on extensive multichannel audio recordings with annotated sound events and their respective locations. In this paper, we introduce a self-supervised approach for SELD adapted from the pre-training methodology of wav2vec 2.0, which learns representations directly from raw audio data, eliminating the need for supervision. By applying this approach to SELD, we can leverage a substantial amount of unlabeled 3D audio data to learn robust representations of sound events and their locations. Our method comprises two primary stages: pre-training and fine-tuning. In the pre-training phase, unlabeled 3D audio datasets are utilized to train our w2v-SELD model, capturing intricate high-level features and contextual information inherent in audio signals. Subsequently, in the fine-tuning stage, a smaller dataset with labeled SELD data fine-tunes the pre-trained model. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed self-supervised approach for SELD. The model surpasses baseline systems provided with the datasets and achieves competitive performance comparable to state-of-the-art supervised methods. The code and pre-trained parameters of our w2v-SELD model are available in this repository.