H‑ear the World

Transform sound into an actionable, meaningful translation layer of the world around you. Describe, share and act upon audio as a spatiotemporal annotated soundscape that empowers you, your business and your AI flow.

Click to sign in and get 10 free ML mins to try our AI analysis

H‑ear describes the World

Easily upload, record or stream media from mobile, tablet, PC, camera, sensor, edge device, etc. into descriptive, actionable, semantic events in time and space. Leverage our globally secure, scaleable, enterprise grade ML, Environmental Audio Classification as a Service capability into your vertical, horizontal, project, lifestyle, research, ... unlock Environmental Audio as a new machine sensor.

MCP Connectors

Claude, OpenClaw, VS Code AI agents

Security Cameras

Ring, Nest, Arlo, Eufy, Tapo, Reolink, Wyze, and more

Any Phone or Tablet

iOS, Android, or any old device

Edge Devices

Raspberry Pi, ESP32, Arduino

Home Assistant

Semantic integration into Home Automation

REST API

Real time Monitoring, Batch, Notifications (Webhooks)

Bulk Import

Process folders of recordings

How It Works

H-ear is Audio Classification at enterprise scale; near real time and enriched for the consumer. H-ear does not teach or train. H-ear uses community ML Classification Models to parse your audio and give you annotation... with a special temporal H‑ear twist.

This is not speech-to-text. H-ear focuses on what is happening, not what is being said

Acquire Audio

Try Record for free, upload media or ask an AI (MCP/API).

Choose Model

Select a ML model; YAMnet, BirdNET, PANNS.

Get Cost Calculation

Receive instant pricing based on your file duration and complexity.

Login & Pay

Trusted login with Google or Microsoft. Secure payment via Stripe.

Get Your Report

H-ear Analysis, Notifications and spatiotemporal, annotated UX.

Acquire Audio

Try Record for free, upload media or ask an AI (MCP/API).

Choose Model

Select a ML model; YAMnet, BirdNET, PANNS.

Get Cost Calculation

Receive instant pricing based on your file duration and complexity.

Login & Pay

Trusted login with Google or Microsoft. Secure payment via Stripe.

Get Your Report

H-ear Analysis, Notifications and spatiotemporal, annotated UX.

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).

0 / 1m 1s

21/26

Standard

Detail

Analysis

Leaflet © OpenStreetMap contributors

Job ID: demo-job

26

Total Events

26

Total Events

40.3

Avg dB

40.3

Avg dB

62.0

Max dB

62.0

Max dB

70%

Avg Confidence

70%

Avg Confidence

YAMNet

Model

YAMNet

Model

Detected Sounds

Animal: 7

Human sounds: 5

Source-ambiguous sounds: 4

Sounds of things: 6

Music: 4

Top Noise Sources

1. Animal > Livestock, farm animals, working animals > Fowl

2 events · 5.8s · 100% conf

Fowl_15

2. Human sounds > Respiratory sounds > Breathing

1 events · 3.8s · 100% conf

Breathing_1

3. Animal > Wild animals > Frog

1 events · 2.9s · 100% conf

Frog_0

Snippet Details

Snippet ID

demo-snippet

Original Filename

demo-60s-fixture-1.mp3

Duration

62.277s (1m 2s)

File Size

973.9 KB

Source Type

Upload

GPS Location

Latitude

-35.250830

Longitude

149.049271

Accuracy

212m

GPS Timestamp

7 Apr 2:31 am

GPS Source

browser

Timezone

Australia/Sydney

Timestamps

Recording Started

7 Apr 2:31 am

Recording Ended

7 Apr 2:32 am

Created At

10 Apr 7:11 pm

Updated At

10 Apr 7:11 pm