Use Cases
We turn your raw audio into a meaningful soundscape— tagged by what, where, and when. With smart sound analysis, we make sound make sense so you can H-ear more, understand more, and do more.
Spot Recording & Complaints
Capture noise events on-the-go with GPS and time tracking
Use Snippets to record noise anywhere from your phone. Each recording is GPS-tagged and timestamped,...
Long-Term Monitoring
Security cameras, property evaluation, and continuous monitoring
Upload/stream recordings from security cameras, weather proof edge sensors, cheap multi-day dictapho...
Industrial & Predictive Maintenance
The silence is the signal — acoustic monitoring for machinery health
Monitor industrial equipment, HVAC systems, pumps, generators, and compressors through their acousti...
Audio Classification as a Service
Roll your own research or audit transcript projects
Leverage YAMNet's 521 audio classes for any non-human sound classification project. Our pay-as-you-g...
Enterprise API Integration
Connect your systems to enterprise-grade audio classification
Integrate powerful audio classification directly into your applications. Our RESTful API delivers sc...
MCP AI Agent Integration
Connect Claude, VS Code, and OpenClaw to enterprise audio classification
Bring H‑ear directly into your AI workflow. Our Model Context Protocol (MCP) server exposes audio cl...
Snippets: Gather, Organize, Report
Mobile-first noise documentation for any scenario
Snippets is your pocket noise monitor. Record from anywhere, see recordings on a map, filter by date...
Spatiotemporal Noise Mapping
Map, timeline, and classify your acoustic environment
Build a living, searchable soundscape of any location. Every sound is pinned to a GPS coordinate, pl...
Birdwatching & Wildlife Monitoring
Passive acoustic monitoring for birders, researchers, and citizen scientists
Turn any microphone into a bird monitoring station. H‑ear's ML classification identifies bird specie...
H-ear your Environment
Play the audio. Interact with the annotation timeline. Download 100% real output and compare H-ear noiseEvents versus ML rawPredictions (we give you both).
Analysis
26
Total Events26
Total Events40.3
Avg dB40.3
Avg dB62.0
Max dB62.0
Max dB70%
Avg Confidence70%
Avg ConfidenceYAMNet
ModelYAMNet
Model1. Animal > Livestock, farm animals, working animals > Fowl
2 events · 5.8s · 100% conf2. Human sounds > Respiratory sounds > Breathing
1 events · 3.8s · 100% conf3. Animal > Wild animals > Frog
1 events · 2.9s · 100% confSnippet Details
62.277s (1m 2s)
973.9 KB
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GPS Location
-35.250830
149.049271
212m
7 Apr 2:31 am
browser
Australia/Sydney
Timestamps
7 Apr 2:31 am
7 Apr 2:32 am
10 Apr 7:11 pm
10 Apr 7:11 pm
Works With Any Audio Source
Upload from security cameras, build edge monitoring stations, or integrate via API
13+ Camera Brands
Edge Devices
Home Assistant
REST API
MCP Agents