# From Computation to Consumption: Exploring the Compute-Energy Link for Training and Testing Neural Networks for SED Systems

arXiv: https://arxiv.org/abs/2409.05080 · HTML: https://mlsanity.com/paper/2409.05080/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 29 references that are in the map.
- First posted on arXiv 8 Sep 2024.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [MusicLM: Generating Music From Text](https://mlsanity.com/paper/2301.11325/) (2023)
- [Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model](https://mlsanity.com/paper/2211.02001/) (2022)
- [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)
- [RAVE: A variational autoencoder for fast and high-quality neural audio synthesis](https://mlsanity.com/paper/2111.05011/) (2021)
- [Energy Consumption of Deep Generative Audio Models](https://mlsanity.com/paper/2107.02621/) (2021)
- [Fusion of Audio and Visual Embeddings for Sound Event Localization and Detection](https://mlsanity.com/paper/2312.09034/) (2023)
- [Description and analysis of novelties introduced in DCASE Task 4 2022 on the baseline system](https://mlsanity.com/paper/2210.07856/) (2022)
- [Performance and energy balance: a comprehensive study of state-of-the-art sound event detection systems](https://mlsanity.com/paper/2310.03455/) (2023)

## Built on by (most cited first, 4 of 4)

- [Energy Consumption Trends in Sound Event Detection Systems](https://mlsanity.com/paper/2409.08763/) (2024)
- [Diffused Responsibility: Analyzing the Energy Consumption of Generative Text-to-Audio Diffusion Models](https://mlsanity.com/paper/2505.07615/) (2025)
- [An Attention-based Feature Memory Design for Energy-Efficient Continual Learning](https://mlsanity.com/paper/2510.04660/) (2025)
- [Energy Variation in Training Modern Computer Vision Architectures](https://mlsanity.com/paper/2610.03772/) (2026)

## Abstract (arXiv)

The massive use of machine learning models, particularly neural networks, has raised serious concerns about their environmental impact. Indeed, over the last few years we have seen an explosion in the computing costs associated with training and deploying these systems. It is, therefore, crucial to understand their energy requirements in order to better integrate them into the evaluation of models, which has so far focused mainly on performance. In this paper, we study several neural network architectures that are key components of sound event detection systems, using an audio tagging task as an example. We measure the energy consumption for training and testing small to large architectures and establish complex relationships between the energy consumption, the number of floating-point operations, the number of parameters, and the GPU/memory utilization.