Fusion of Audio and Visual Embeddings for Sound Event Localization and Detection

Davide Berghi; Peipei Wu; Jinzheng Zhao; Wenwu Wang; Philip J. B. Jackson

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arXiv · 2312.09034

Fusion of Audio and Visual Embeddings for Sound Event Localization and Detection

Davide Berghi·Peipei Wu·Jinzheng Zhao·Wenwu Wang·Philip J. B. Jackson

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Abstract

Sound event localization and detection (SELD) combines two subtasks: sound event detection (SED) and direction of arrival (DOA) estimation. SELD is usually tackled as an audio-only problem, but visual information has been recently included. Few audio-visual (AV)-SELD works have been published and most employ vision via face/object bounding boxes, or human pose keypoints. In contrast, we explore the integration of audio and visual feature embeddings extracted with pre-trained deep networks. For the visual modality, we tested ResNet50 and Inflated 3D ConvNet (I3D). Our comparison of AV fusion methods includes the AV-Conformer and Cross-Modal Attentive Fusion (CMAF) model. Our best models outperform the DCASE 2023 Task3 audio-only and AV baselines by a wide margin on the development set of the STARSS23 dataset, making them competitive amongst state-of-the-art results of the AV challenge, without model ensembling, heavy data augmentation, or prediction post-processing. Such techniques and further pre-training could be applied as next steps to improve performance.

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BibTeX RIS

Davide Berghi, Peipei Wu, Jinzheng Zhao, Wenwu Wang, Philip J. B. Jackson. 2023-12-14. Fusion of Audio and Visual Embeddings for Sound Event Localization and Detection. https://arxiv.org/abs/2312.09034

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