Application of Deep Learning-Based Voice Activity Detection (VAD) Models in Intelligent Monitoring of Microbial Environments and Early Detection of Biocontamination Patterns

Deep learning-based Voice Activity Detection (VAD) is emerging as a transformative approach for intelligent microbial environment monitoring and early-stage bio-contamination detection. This study explores the potential of acoustic artificial intelligence to identify biologically relevant environmental changes in biosafety-sensitive settings, such as healthcare facilities, agricultural systems, biotechnology laboratories, and industrial monitoring environments. The objective is to assess how deep neural architectures can improve the detection of subtle acoustic and vibrational signals associated with respiratory anomalies, microbial growth, contamination spread, and abnormal environmental interactions, particularly where traditional threshold-based or statistical methods are insufficient. The methodology involves a synthesis of recent literature regarding deep learning, intelligent sensing, and acoustic biosurveillance. The study examines how architectures-such as CNN-LSTM hybrids, attention-based networks, and transformers-process complex sound patterns within noisy and dynamic environments. Furthermore, it considers the integration of VAD with IoT sensors, cloud platforms, and environmental monitoring systems to support real-time detection and automated response. Findings from the literature indicate that deep learning-based VAD systems consistently outperform conventional methods in recognizing biologically meaningful acoustic patterns, thereby strengthening early warning mechanisms. These systems enhance sensitivity, robustness, and operational efficiency across domains including medical monitoring, food safety, and agricultural biosecurity. However, significant challenges remain, including high computational complexity, limitations in explainability, environmental variability, and a scarcity of standardized datasets. In conclusion, deep learning-based VAD represents a vital direction for future biosurveillance. Its full potential will rely on the development of multimodal, lightweight, and explainable AI systems that integrate acoustic analysis with thermal imaging, biosensors, robotics, and environmental intelligence platforms. Such advancements could redefine smart environmental monitoring and facilitate the creation of more autonomous and proactive biosafety infrastructures.

کلیدواژه ها:

Deep learning ، voice activity detection ، biosurveillance ، microbial monitoring ، multimodal artificial intelligence