Machine Learning-Enabled Triboelectric Nanogenerator for Continuous Sound Monitoring and Captioning

Abstract

Advancements in live audio processing, specifically in sound classification and audio captioning technologies, have widespread applications ranging from surveillance to accessibility services. However, traditional methods encounter scalability and energy efficiency challenges. To overcome these, Triboelectric Nanogenerators (TENG) are explored for energy harvesting, particularly in live-streaming sound monitoring systems. This study introduces a sustainable methodology integrating TENG-based sensors into live sound monitoring pipelines, enhancing energy-efficient sound classification and captioning by model selection and fine-tuning strategies. Our cost-effective TENG sensor harvests ambient sound vibrations and background noise, producing up to 1.2 µW cm⁻² output power and successfully charging capacitors. This shows its capability for sustainable energy harvesting. The system achieves 94.3% classification accuracy using the Hierarchical Token Semantic Audio Transformer (HTS-AT) model identified as optimal for live sound event monitoring. Additionally, continuous audio captioning using the EnCodec Combining Neural Audio Codec and Audio-Text Joint Embedding for Automated Audio Captioning model (EnCLAP) showcases rapid and precise processing capabilities that are suitable for live-streaming environments. The Bidirectional Encoder representation from the Audio Transformers (BEATs) model also demonstrated exceptional performance, achieving an accuracy of 97.25%. These models were fine-tuned using the TENG-recorded ESC-50 dataset, ensuring the system's adaptability to diverse sound conditions. Overall, this research significantly contributes to the development of energy-efficient sound monitoring systems with wide-ranging implications across various sectors.