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Enhanced Sound Event Localization and Detection in Real 360-degree audio-visual soundscapes

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arxiv 2401.17129 v1 pith:WCKI535E submitted 2024-01-29 cs.SD cs.AIeess.AS

Adrian S. Roman , Baladithya Balamurugan , Rithik Pothuganti This is my paper · ORCID
classification cs.SDcs.AIeess.AS
keywords audio-visualaudio-onlybuilddatadetectionenhancedeventlocalization
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This technical report details our work towards building an enhanced audio-visual sound event localization and detection (SELD) network. We build on top of the audio-only SELDnet23 model and adapt it to be audio-visual by merging both audio and video information prior to the gated recurrent unit (GRU) of the audio-only network. Our model leverages YOLO and DETIC object detectors. We also build a framework that implements audio-visual data augmentation and audio-visual synthetic data generation. We deliver an audio-visual SELDnet system that outperforms the existing audio-visual SELD baseline.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Integrating Spatial and Semantic Embeddings for Stereo Sound Event Localization in Videos

    eess.AS 2025-09 conditional novelty 5.0 of 10

    A Cross-Modal Conformer that fuses CLAP audio and OWL-ViT visual embeddings with a CNN-Conformer SELD backbone, trained on large synthetic data, ranks second in DCASE 2025 Task 3 Track B.

  2. Spatial and Semantic Embedding Integration for Stereo Sound Event Localization and Detection in Regular Videos

    eess.AS 2025-07 conditional novelty 5.0 of 10

    Fusing frozen CLAP and OWL-ViT embeddings via a Cross-Modal Conformer, plus autocorrelation-based features, improves stereo SELD over DCASE 2025 baselines.

  3. MVANet: Multi-Stage Video Attention Network for Sound Event Localization and Detection with Source Distance Estimation

    eess.AS 2024-11 conditional novelty 4.0 of 10

    MVANet applies multi-stage audio-guided video attention to improve audio-visual 3D sound event localization and source distance estimation on STARSS23.

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