Technology Stack

Cutting-edge components working in harmony

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IoT Sensor Network

High-precision acoustic sensors deployed across your environment, capturing real-time sound data with 95% accuracy. Weather-resistant and maintenance-free.

Class 1 Accuracy 24/7 Monitoring 5-Year Battery
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AI Analysis Engine

Machine learning algorithms identify noise sources, predict patterns, and generate prioritized mitigation recommendations automatically.

Deep Learning Pattern Recognition Auto-Classification
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Cloud Platform

Scalable cloud infrastructure processes millions of data points, ensuring fast access and reliable performance for any size deployment.

99.9% Uptime Auto-Scaling Secure Storage
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Visualization Dashboard

Intuitive web-based interface with interactive heat maps, trend charts, and customizable reports for stakeholders at all levels.

Real-Time Updates Custom Reports Mobile Access

How It Works

From data collection to actionable insights

1

Data Collection

Strategically placed sensors continuously measure sound levels (dB), frequency spectrum, and environmental context across your entire area.

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2

AI Analysis

Machine learning models process incoming data, identifying noise sources (traffic, construction, industrial), patterns, and anomalies in real-time.

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3

Priority Mapping

Algorithm combines noise levels with population density, sensitive locations (schools, hospitals), and time-of-day to create priority heat maps.

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4

Recommendations

System generates evidence-based mitigation strategies tailored to each location: noise barriers, traffic rerouting, zoning changes, or operational adjustments.

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5

Continuous Monitoring

Track implementation effectiveness with before/after analysis and ongoing monitoring to ensure long-term success.

Advanced Capabilities

🎯 Smart Prioritization

Our AI doesn't just identify loud areasβ€”it understands context. A 75dB reading near a highway at 3pm has different implications than the same level near a hospital at 3am. Our priority algorithm factors in:

  • Population density and demographics
  • Sensitive location proximity (schools, hospitals, libraries)
  • Time-of-day and day-of-week patterns
  • Baseline vs. anomaly detection
  • Historical trend analysis

πŸ“ˆ Predictive Modeling

Machine learning models trained on historical data can forecast future noise challenges before they become problems. Applications include:

  • Traffic growth impact predictions
  • Seasonal variation forecasting
  • Development project noise assessment
  • Event planning and management
  • Long-term mitigation strategy planning

πŸ”— System Integration

CityNoise.tech seamlessly connects with your existing infrastructure through open APIs and standard protocols:

  • GIS and mapping systems integration
  • Traffic management platforms
  • Citizen complaint databases
  • Environmental monitoring networks
  • Smart city IoT ecosystems

πŸ‘₯ Community Engagement

Our mobile app and web portal empower citizens to participate actively in noise management:

  • Real-time noise level viewing
  • Incident reporting with photo/audio evidence
  • Complaint tracking and status updates
  • Educational resources about noise pollution
  • Impact visualization of completed projects

Technical Specifications

Sensor Specifications

Measurement Range 30 - 130 dB
Frequency Range 20 Hz - 20 kHz
Accuracy Β±1.5 dB (Class 1)
Sampling Rate 48 kHz
Operating Temperature -20Β°C to 60Β°C
Weatherproofing IP67 rated

Platform Performance

Data Processing Latency < 500ms
System Uptime 99.9% SLA
Concurrent Users Unlimited
Data Retention 5 years default
API Response Time < 200ms
Security SOC 2 Type II, ISO 27001

Experience the Technology

Schedule a live demo to see our platform in action.