Towards joint sound scene and polyphonic sound event recognition
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Abstract (arXiv)
Acoustic Scene Classification (ASC) and Sound Event Detection (SED) are two separate tasks in the field of computational sound scene analysis. In this work, we present a new dataset with both sound scene and sound event labels and use this to demonstrate a novel method for jointly classifying sound scenes and recognizing sound events. We show that by taking a joint approach, learning is more efficient and whilst improvements are still needed for sound event detection, SED results are robust in a dataset where the sample distribution is skewed towards sound scenes.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Apr 2019.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 1.
5 papers.
- 2021Improving weakly supervised sound event detection with self-supervised auxiliary taskscited by 4
- 2023DiffSED: Sound Event Detection with Denoising Diffusioncited by 4
- 2021Joint framework with deep feature distillation and adaptive focal loss for weakly supervised audio tagging and acoustic event detectioncited by 1
- 2021Environmental sound analysis with mixup based multitask learning and cross-task fusioncited by 0
- 2022Impact of Acoustic Event Tagging on Scene Classification in a Multi-Task Learning Frameworkcited by 0
- 2023DiffSED: Sound Event Detection with Denoising Diffusion14 Aug 2023
- 2022Impact of Acoustic Event Tagging on Scene Classification in a Multi-Task Learning Framework27 Jun 2022
- 2021Improving weakly supervised sound event detection with self-supervised auxiliary tasks12 Jun 2021
- 2021Environmental sound analysis with mixup based multitask learning and cross-task fusion30 Mar 2021
- 2021Joint framework with deep feature distillation and adaptive focal loss for weakly supervised audio tagging and acoustic event detection23 Mar 2021