# Climate Change & Computer Audition: A Call to Action and Overview on Audio Intelligence to Help Save the Planet

arXiv: https://arxiv.org/abs/2203.06064 · HTML: https://mlsanity.com/paper/2203.06064/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 171 references that are in the map.
- First posted on arXiv 10 Mar 2022.

## Builds on (most cited first, 24 of 24)

- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [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)
- [Underspecification Presents Challenges for Credibility in Modern Machine Learning](https://mlsanity.com/paper/2011.03395/) (2020)
- [What is being transferred in transfer learning?](https://mlsanity.com/paper/2008.11687/) (2020)
- [Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders](https://mlsanity.com/paper/1704.01279/) (2017)
- [Supervised Speech Separation Based on Deep Learning: An Overview](https://mlsanity.com/paper/1708.07524/) (2017)
- [Towards Physics-informed Deep Learning for Turbulent Flow Prediction](https://mlsanity.com/paper/1911.08655/) (2019)
- [A Survey on Dialogue Systems: Recent Advances and New Frontiers](https://mlsanity.com/paper/1711.01731/) (2017)
- [Federated Learning for Open Banking](https://mlsanity.com/paper/2108.10749/) (2021)
- [The Ethics of AI Ethics -- An Evaluation of Guidelines](https://mlsanity.com/paper/1903.03425/) (2019)
- [Deep Learning for Audio Signal Processing](https://mlsanity.com/paper/1905.00078/) (2019)
- [Privacy in Deep Learning: A Survey](https://mlsanity.com/paper/2004.12254/) (2020)
- [A Wavenet for Speech Denoising](https://mlsanity.com/paper/1706.07162/) (2017)
- [Automatic acoustic detection of birds through deep learning: the first Bird Audio Detection challenge](https://mlsanity.com/paper/1807.05812/) (2018)
- [The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates](https://mlsanity.com/paper/2102.13468/) (2021)
- [Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets](https://mlsanity.com/paper/1605.01156/) (2016)
- [Zero-Shot Audio Classification via Semantic Embeddings](https://mlsanity.com/paper/2011.12133/) (2020)
- [DeepSpectrumLite: A Power-Efficient Transfer Learning Framework for Embedded Speech and Audio Processing from Decentralised Data](https://mlsanity.com/paper/2104.11629/) (2021)
- [Fairness and underspecification in acoustic scene classification: The case for disaggregated evaluations](https://mlsanity.com/paper/2110.01506/) (2021)
- [The Orchive : Data mining a massive bioacoustic archive](https://mlsanity.com/paper/1307.0589/) (2013)
- [N-HANS: Introducing the Augsburg Neuro-Holistic Audio-eNhancement System](https://mlsanity.com/paper/1911.07062/) (2019)

## Abstract (arXiv)

Among the seventeen Sustainable Development Goals (SDGs) proposed within the 2030 Agenda and adopted by all the United Nations member states, the 13th SDG is a call for action to combat climate change for a better world. In this work, we provide an overview of areas in which audio intelligence -- a powerful but in this context so far hardly considered technology -- can contribute to overcome climate-related challenges. We categorise potential computer audition applications according to the five elements of earth, water, air, fire, and aether, proposed by the ancient Greeks in their five element theory; this categorisation serves as a framework to discuss computer audition in relation to different ecological aspects. Earth and water are concerned with the early detection of environmental changes and, thus, with the protection of humans and animals, as well as the monitoring of land and aquatic organisms. Aerial audio is used to monitor and obtain information about bird and insect populations. Furthermore, acoustic measures can deliver relevant information for the monitoring and forecasting of weather and other meteorological phenomena. The fourth considered element is fire. Due to the burning of fossil fuels, the resulting increase in CO2 emissions and the associated rise in temperature, fire is used as a symbol for man-made climate change and in this context includes the monitoring of noise pollution, machines, as well as the early detection of wildfires. In all these areas, computer audition can help counteract climate change. Aether then corresponds to the technology itself that makes this possible. This work explores these areas and discusses potential applications, while positioning computer audition in relation to methodological alternatives.