# Towards joint sound scene and polyphonic sound event recognition

arXiv: https://arxiv.org/abs/1904.10408 · HTML: https://mlsanity.com/paper/1904.10408/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2019.

## Built on by (most cited first, 5 of 5)

- [Improving weakly supervised sound event detection with self-supervised auxiliary tasks](https://mlsanity.com/paper/2106.06858/) (2021)
- [DiffSED: Sound Event Detection with Denoising Diffusion](https://mlsanity.com/paper/2308.07293/) (2023)
- [Joint framework with deep feature distillation and adaptive focal loss for weakly supervised audio tagging and acoustic event detection](https://mlsanity.com/paper/2103.12388/) (2021)
- [Environmental sound analysis with mixup based multitask learning and cross-task fusion](https://mlsanity.com/paper/2103.16079/) (2021)
- [Impact of Acoustic Event Tagging on Scene Classification in a Multi-Task Learning Framework](https://mlsanity.com/paper/2206.13476/) (2022)

## 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.