SAMPLE ML/ DL Data | 10 M POI Measurements of Urban Venues | 35 B + Data Points | 100% Traceable ...

Connect with our experts for Street and Venue Noise-Level Data. Unlock unique insights into the real-world acoustic environment of cities and venues across 180+ countries. Silencio has built the world’s largest database on noise levels, statistically interpolated using over 35 billion datapoints, developed in collaboration with leading acoustics professionals. Unlike traditional models that rely solely on computed estimations, our dataset uniquely combines real-world measurements with AI-driven predictions to deliver the most accurate and reliable noise-level data available today.

Maximize AI Performance with the World’s Largest Real-World Noise-Level Dataset

What sets our dataset apart? Silencio’s Street and Venue Noise-Level Data is the world’s largest and most accurate collection of real-world acoustic data, combining over 35 billion datapoints with AI-driven interpolation, developed together with professional acousticians. Unlike synthetic models, our dataset integrates real measurements and AI predictions to provide unparalleled ground truth for AI training.

Designed for AI Applications:
Empower your AI models with high-quality, diverse, and realistic acoustic data. Ideal for training AI in sound recognition, noise mapping, autonomous systems, smart cities, mobility intelligence, and beyond.

Reliable & Compliant:
Collected through our mobile app with explicit user consent, fully anonymized, and fully GDPR-compliant, ensuring ethical sourcing and regulatory alignment.

Historical & Real-Time:
Train models using both historical and continuously updated data to improve accuracy and robustness over time and across regions.

Granular & Customizable:
Globally available, highly granular, and adaptable to your AI pipeline needs — from raw acoustic datapoints to aggregated sound profiles.

Simple Integration:
Delivered via CSV exports or S3 bucket delivery (APIs coming soon), allowing smooth integration into your existing AI training workflows.