How it works:

• Data Acquisition: Capture audio data from microphones in real time.
• Preprocessing: Enhance audio quality and remove noise for optimal SNR.
• Feature Extraction: Apply advanced signal processing techniques to extract relevant audio features.
• Detection: Use DNN models to detect sounds indicating human presence (e.g., footsteps).
• Classification: Classify detected sounds into categories (e.g., footsteps, speech, environmental noise) using machine learning models.
• Decision Making: Determine the likelihood of human presence and potential ambush threats based on classified sounds.
• Reporting: Generate real-time alerts and reports detailing detected sounds and potential threats.
• Feedback Loop: Continuously improve AI models based on feedback to enhance detection accuracy and efficiency over time.

Key Features:

• Advanced Audio Detection
• High Accuracy and Precision
• Real-Time Processing
• Versatility and Integration
• Continuous Improvement
• Edge Device Deployment

Technical Skills:

• Advanced Signal Processing
• DNN
• Computer Vision
• Image Processing
• Edge Devices
• TensorFlow
• PyTorch
• Embedded Systems

Technical Specifications:

• Compatibility: Compatible with various security and surveillance systems and edge device deployments.

Client:

• In House