Exploring Green AI for Audio Deepfake Detection

The state-of-the-art audio deepfake detectors leveraging deep neural networks exhibit impressive recognition performance. Nonetheless, this advantage is accompanied by a significant carbon footprint. This is mainly due to the use of high-performance computing with accelerators and high training time. Studies show that average deep NLP model produces around 626k lbs of CO\textsubscript{2} which is equivalent to five times of average US car emission at its lifetime. This is certainly a massive threat to the environment. To tackle this challenge, this study presents a novel framework for audio deepfake detection that can be seamlessly trained using standard CPU resources. Our proposed framework utilizes off-the-shelve self-supervised learning (SSL) based models which are pre-trained and available in public repositories. In contrast to existing methods that fine-tune SSL models and employ additional deep neural networks for downstream tasks, we exploit classical machine learning algorithms such as logistic regression and shallow neural networks using the SSL embeddings extracted using the pre-trained model. Our approach shows competitive results compared to the commonly used high-carbon footprint approaches. In experiments with the ASVspoof 2019 LA dataset, we achieve a 0.90\% equal error rate (EER) with less than 1k trainable model parameters. To encourage further research in this direction and support reproducible results, the Python code will be made publicly accessible following acceptance. Github: https://github.com/sahasubhajit/Speech-Spoofing-

Exploring IoT for real-time CO2 monitoring and analysis

As a part of this project, we have developed an IoT-based instrument utilizing the NODE MCU-ESP8266 module, MQ135 gas sensor, and DHT-11 sensor for measuring CO$_2$ levels in parts per million (ppm), temperature, and humidity. The escalating CO$_2$ levels worldwide necessitate constant monitoring and analysis to comprehend the implications for human health, safety, energy efficiency, and environmental well-being. Thus, an efficient and cost-effective solution is imperative to measure and transmit data for statistical analysis and storage. The instrument offers real-time monitoring, enabling a comprehensive understanding of indoor environmental conditions. By providing valuable insights, it facilitates the implementation of measures to ensure health and safety, optimize energy efficiency, and promote effective environmental monitoring. This scientific endeavor aims to contribute to the growing body of knowledge surrounding CO$_2$ levels, temperature, and humidity, fostering sustainable practices and informed decision-making