# Description and analysis of novelties introduced in DCASE Task 4 2022 on the baseline system

arXiv: https://arxiv.org/abs/2210.07856 · HTML: https://mlsanity.com/paper/2210.07856/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2022.

## Built on by (most cited first, 7 of 7)

- [From Computation to Consumption: Exploring the Compute-Energy Link for Training and Testing Neural Networks for SED Systems](https://mlsanity.com/paper/2409.05080/) (2024)
- [Dynamic Convolutional Neural Networks as Efficient Pre-trained Audio Models](https://mlsanity.com/paper/2310.15648/) (2023)
- [Dual Knowledge Distillation for Efficient Sound Event Detection](https://mlsanity.com/paper/2402.02781/) (2024)
- [Energy Consumption Trends in Sound Event Detection Systems](https://mlsanity.com/paper/2409.08763/) (2024)
- [On the choice of the optimal temporal support for audio classification with Pre-trained embeddings](https://mlsanity.com/paper/2312.14005/) (2023)
- [Normalizing Energy Consumption for Hardware-Independent Evaluation](https://mlsanity.com/paper/2409.05602/) (2024)
- [Exposing the Cost of Deep Learning Audio Development](https://mlsanity.com/paper/2610.01619/) (2026)

## Abstract (arXiv)

The aim of the Detection and Classification of Acoustic Scenes and Events Challenge Task 4 is to evaluate systems for the detection of sound events in domestic environments using an heterogeneous dataset. The systems need to be able to correctly detect the sound events present in a recorded audio clip, as well as localize the events in time. This year's task is a follow-up of DCASE 2021 Task 4, with some important novelties. The goal of this paper is to describe and motivate these new additions, and report an analysis of their impact on the baseline system. We introduced three main novelties: the use of external datasets, including recently released strongly annotated clips from Audioset, the possibility of leveraging pre-trained models, and a new energy consumption metric to raise awareness about the ecological impact of training sound events detectors. The results on the baseline system show that leveraging open-source pretrained on AudioSet improves the results significantly in terms of event classification but not in terms of event segmentation.