  In this paper, we propose a solution for improving the quality of temporal
sound localization. We employ a multimodal fusion approach to combine visual
and audio features. High-quality visual features are extracted using a
state-of-the-art self-supervised pre-training network, resulting in efficient
video feature representations. At the same time, audio features serve as
complementary information to help the model better localize the start and end
of sounds. The fused features are trained in a multi-scale Transformer for
training. In the final test dataset, we achieved a mean average precision (mAP)
of 0.33, obtaining the second-best performance in this track.
