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 analysing 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.

使用知识蒸馏技术设计高效、轻量级的学生模型进行生物声学中的语音检测，并与 EcoVAD 教师模型进行对比分析，结果显示蒸馏模型表现出与教师模型相当的性能，为实时生态监测提供了有希望的方法。

利用声学识别和知识蒸馏在环境音频中实现高效的语音检测

Efficient speech detection in environmental audio using acoustic  recognition and knowledge distillation

To stay competitive in the growing dairy market, farmers must continuously
improve their livestock production systems. Precision livestock farming
technologies provide individualised monitoring of animals on commercial farms,
optimising livestock production. Continuous acoustic monitoring is a widely
accepted sensing technique used to estimate the daily rumination and grazing
time budget of free-ranging cattle. However, typical environmental and natural
noises on pasture noticeably affect the performance and generalisation of
current acoustic methods. In this study, we present an acoustic method called
Noise-Robust Foraging Activity Recognizer (NRFAR). The proposed method
determines foraging activity bouts by analysing fixed-length segments of
identified jaw movement events associated with grazing and rumination. The
additive noise robustness of NRFAR was evaluated for several signal-to-noise
ratios, using stationary Gaussian white noise and four different non-stationary
natural noise sources. In noiseless conditions, NRFAR reaches an average
balanced accuracy of 89%, outperforming two previous acoustic methods by more
than 7%. Additionally, NRFAR presents better performance than previous acoustic
methods in 66 out of 80 evaluated noisy scenarios (p<0.01). NRFAR operates
online with a similar computational cost to previous acoustic methods. The
combination of these properties and the high performance in harsh free-ranging
environments render NRFAR an excellent choice for real-time implementation in a
low-power embedded device. The instrumentation and computational algorithms
presented within this publication are protected by a pending patent
application: AR P20220100910. Web demo available at:
this https URL

本研究提出了噪声鲁棒的饲草活动识别器 (NRFAR)，通过分析固定长度的下颌运动事件识别牛的觅食活动，经过在不同噪声环境下的验证，NRFAR 在牛在自由放牧环境下的实时应用中具有显著的性能优势，能够更好地优化畜牧生产。