# Energy Consumption Trends in Sound Event Detection Systems

arXiv: https://arxiv.org/abs/2409.08763 · HTML: https://mlsanity.com/paper/2409.08763/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 22 references that are in the map.
- First posted on arXiv 13 Sep 2024.

## Builds on (most cited first, 15 of 15)

- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning](https://mlsanity.com/paper/2002.05651/) (2020)
- [The Computational Limits of Deep Learning](https://mlsanity.com/paper/2007.05558/) (2020)
- [Compute Trends Across Three Eras of Machine Learning](https://mlsanity.com/paper/2202.05924/) (2022)
- [Deep Learning for Audio Signal Processing](https://mlsanity.com/paper/1905.00078/) (2019)
- [Chasing Carbon: The Elusive Environmental Footprint of Computing](https://mlsanity.com/paper/2011.02839/) (2020)
- [A Framework for the Robust Evaluation of Sound Event Detection](https://mlsanity.com/paper/1910.08440/) (2019)
- [Energy Consumption of Deep Generative Audio Models](https://mlsanity.com/paper/2107.02621/) (2021)
- [DCASE 2024 Task 4: Sound Event Detection with Heterogeneous Data and Missing Labels](https://mlsanity.com/paper/2406.08056/) (2024)
- [Description and analysis of novelties introduced in DCASE Task 4 2022 on the baseline system](https://mlsanity.com/paper/2210.07856/) (2022)
- [Training sound event detection with soft labels from crowdsourced annotations](https://mlsanity.com/paper/2302.14572/) (2023)
- [Performance and energy balance: a comprehensive study of state-of-the-art sound event detection systems](https://mlsanity.com/paper/2310.03455/) (2023)
- [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)
- [Normalizing Energy Consumption for Hardware-Independent Evaluation](https://mlsanity.com/paper/2409.05602/) (2024)

## Built on by (most cited first, 2 of 2)

- [Diffused Responsibility: Analyzing the Energy Consumption of Generative Text-to-Audio Diffusion Models](https://mlsanity.com/paper/2505.07615/) (2025)
- [Exposing the Cost of Deep Learning Audio Development](https://mlsanity.com/paper/2610.01619/) (2026)

## Abstract (arXiv)

Deep learning systems have become increasingly energy- and computation-intensive, raising concerns about their environmental impact. As organizers of the Detection and Classification of Acoustic Scenes and Events (DCASE) challenge, we recognize the importance of addressing this issue. For the past three years, we have integrated energy consumption metrics into the evaluation of sound event detection (SED) systems. In this paper, we analyze the impact of this energy criterion on the challenge results and explore the evolution of system complexity and energy consumption over the years. We highlight a shift towards more energy-efficient approaches during training without compromising performance, while the number of operations and system complexity continue to grow. Through this analysis, we hope to promote more environmentally friendly practices within the SED community.