  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.
