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Efficient Speech Detection in Environmental Audio Using Acoustic Recognition and Knowledge Distillation

Priebe,Drew
; 
Ghani,Burooj
; 
Stowell,Dan
Abstract
The ongoing biodiversity crisis, driven by factors such as land-use change and global warming, emphasizes the need for effective ecological monitoring methods. Acoustic monitoring of biodiversity has emerged as an important monitoring tool. Detecting human voices in soundscape monitoring projects is useful both for analyzing human disturbance and for privacy filtering. Despite significant strides in deep learning in recent years, the deployment of large neural networks on compact devices poses challenges due to memory and latency constraints. Our approach focuses on leveraging knowledge distillation techniques to design efficient, lightweight student models for speech detection in bioacoustics. In particular, we employed the MobileNetV3-Small-Pi model to create compact yet effective student architectures to compare against the larger EcoVAD teacher model, a well-regarded voice detection architecture in eco-acoustic monitoring. The comparative analysis included examining various configurations of the MobileNetV3-Small-Pi-derived student models to identify optimal performance. Additionally, a thorough evaluation of different distillation techniques was conducted to ascertain the most effective method for model selection. Our findings revealed that the distilled models exhibited comparable performance to the EcoVAD teacher model, indicating a promising approach to overcoming computational barriers for real-time ecological monitoring.
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Publisher Copyright: © 2024 by the authors.
Date
2024-04
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Tilburg University Research Output Collection
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sensors-24-02046-v2.pdf
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Keywords
bioacoustics, classification, deep learning, eco-acoustics, knowledge distillation, passive acoustic monitoring, speech detection, transfer learning
Citation
Priebe, D, Ghani, B & Stowell, D 2024, 'Efficient Speech Detection in Environmental Audio Using Acoustic Recognition and Knowledge Distillation', Sensors, vol. 24, no. 7, 2046. https://doi.org/10.3390/s24072046
URI
https://hdl.handle.net/20.500.14602/81008
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