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Smart Home

Security & Monitoring

Security cameras 

Video doorbells

Building surveillance

Home Entertainment

Smart speakers

Remote controls

Set-top boxes

Smart displays


Video entertainment

Home Controls

Door locks


Smart lighting

Motion & pressure sensors 

Smart Appliances


White goods

Fitness Equipment

Sample Applications Deployed in Complex Environments

Speech as an interface
Noise suppression

Echo cancellation

Glass break detection​

Face recognition

Person & package detection

Vehicle detection


Gesture recognition

Pose classification

Motion & pressure sensors

T3/T4 alarms

Low-battery alerts 

Use Case: Robust Window Glass Break Detection

Deep learning enables the detection of external window breaking while ignoring other noises within the environment of the device. 

  • Existing solutions look only for a noise level at a specific frequency range.

  • This misses many glass break events (false negatives). and triggers other loud noises that are not glass (false positive).

Syntiant has collected thousands of examples of external home glass breaking and trained advanced neural networks to trigger only when an external glass break is detected while ignoring all other noises, including similar sounding events such as plates breaking in the kitchen. 

Use Case: Pet Detection

Using NDP200 and a low-cost Pixart camera, trained up a MobileNetV1 0.25 person detection model.

  • Input size 320x240 grayscale.

  • Used pet/non-pet images from MS COCO for demo network.

  • Achieved 90% accuracy on the demo model after less than 5 hours of training.

**Pet detection on NDP200 can run for weeks on a single car battery.

Use Case: Local voice command of iRobot Roomba

On-device command of  medium-sized home appliance, enabled by the NDP101®.

  • Use of microphones with voice activity detection allows NDP to stay at ultra-low-energy standby mode and only wakes up when alerted by sound 

Use Case: Hands Free Remote Control

The NDP120 adds a robust voice interface to a remote control while not draining the battery.

  • Noise Suppression and energy management: Low energy implementation of always-on wake word detection in a noisy home environment

    • Syntiant’s processors and models are based on advanced convolutional neural networks to trigger only when a wake word is heard

    • Noise suppression improves wake-word performance even at low SNR (signal to noise ratio) environments

    • Use of microphones with voice activity detection allows NDP to stay at ultra-low-energy standby mode and only wakes up when alerted by sound 

*Wake word detection on NDP120 runs for 12 mo. on a 2x AA battery

Use Case: Speech as an Interface

Ideal for smart speakers: low power, hands-free operation, even in the presence of noise. 

  • Far-field always-on voice allows for wake-word detection, even in the presence of very loud music at very low power: ideal for smart speakers. 

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