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SilentCity
AI Urban Noise Monitor based on ESP32-C3
Solar-powered noise sensor that identifies noise sources (construction, traffic, nightlife) for urban planning compliance.
SPL Accuracy
±0.5 dB
Source Classification
88%
SilentCity
Client
A municipal environmental agency deploying a city-wide noise monitoring network to enforce zoning compliance and respond to citizen noise complaints.
Challenge
Accurately measuring sound pressure levels (IEC 61672 Class 2) and classifying the noise source type (construction, traffic, nightlife, nature) at the edge, while operating on solar power.
Product Concept & Architecture
The ESP32-C3 RISC-V SoC reads a TDK INMP441 digital MEMS microphone via I2S. A solar-charged 18650 battery provides 24/7 operation.
Engineering Solution
The I2S interface captures 32-bit audio at 48kHz. The ESP32-C3 computes A-weighted SPL (Leq, L10, L90) per international standard every second. A TinyML audio spectrogram CNN classifies the source into 8 categories using 2-second windows. Only the 5-minute aggregate statistics are transmitted via Wi-Fi.
Result
IEC 61632 Class 2-compliant SPL accuracy. 88% noise source classification accuracy. 100% solar-powered.
Deep Tech FAQ
What makes the AI Urban Noise Monitor based on ESP32-C3 unique?+
The AI Urban Noise Monitor based on ESP32-C3 leverages custom hardware and edge AI to deliver industry-leading performance and efficiency.
What are the core technical benefits?+
Key benefits include low power consumption, real-time processing, robust connectivity, and a compact form factor tailored for deployment.
Can this architecture be adapted for other use cases?+
Absolutely. The underlying methodology is modular and can be retargeted for various applications with minimal redesign.
Project Profile
Services Delivered
Hardware DesignEmbedded Software
Core Technologies
Wi-Fi/BluetoothTinyMLRTOS
Silicon & Vendors
EspressifTDK
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