After
Before
Key Features
- Deep learning based RNS
- Small footprint and low MIPS
- All sample rates are supported (eg. 8Khz/16Khz/48Khz)
- Search & Repair
- Works with single MIC
- Optimized for high-performance end-to-end voice pipelines
Uniqueness
Custom Deep Neural Network
Works for Stationary & non-Stationary noise
Low latency (<25 ms)
Scalable from MCUs to FPGAs to SoCs
Use cases
Septra
Communication Devices
(SmartPhone, Walkie-talkie, VOIP, wearables)
Septra
Teleconferencing
Septra
Human to Machine Communication
Demos
Real-Time Noise Suppression
over
a VOIP call
Key Features
- Edge-based Voice Command recognition engine (No Internet connection required)
- Deep Learning based algorithm
- With Wake-word support
- High recognition rate in noisy environment
- Supports multiword voice commands (1 to 3 words in single command)
- Ultra low memory footprint (<38 KB RAM)
- Very low MCPS (<70 MHz on Cortex M4 CPU)
- Support for multiple languages
Uniqueness
Accuracy
Works well in noisy environments. Coupled with our noise suppression engine, recognition rates higher than 95% are consistently achieved.
Requires Minimal Voice Samples
Our unique audio data preparation technology expands a minimal set of original voice samples to a synthetic dataset that is orders of magnitude larger. This data preparation tool is part of user software and allows infield training.
Enabling “Tiny ML” class of applications
Our AI solutions are designed specifically for low-cost, low-power edge devices built using MCU, DSP and FPGA. With ultra-low memory footprint, customer applications have access to more RAM.
Use cases
Septra
Smart Home
Septra
Automotive
Septra
Industrial equipment
Septra
Speakers & Headphones
Septra
Audio-video devices
Case Studies
Demos
Voice Command Recognition on Renesas MCU
Sound Type Identification
IGN-SEC enables the classification of ambient sound allowing precise identification of various sound types. The underlying algorithms are accurate enough to discriminate between very similar sound types (eg. two different sirens, the bark of two different dog breeds, etc.)
Anomalous Sound Detection:
Anomalies in operation of equipments & infrastructure can be caught early on, by analysing the sounds picked up by microphones installed on or close to the equipment. IGN-SEC then categorizes the picked-up audio as normal or abnormal, allowing early failure prediction of these machines.
Markets:
Automotive
Consumer Electronics
Industrial
Surveillance
After
Before
Key Features
- Deep learning based RNS
- Small footprint and low MIPS
- All sample rates are supported (eg. 8Khz/16Khz/48Khz)
- Search & Repair
- Works with single MIC
- Optimized for high-performance end-to-end voice pipelines
Uniqueness
Custom Deep Neural Network
Works for Stationary & non-Stationary noise
Low latency (<25 ms)
Scalable from MCUs to FPGAs to SoCs
Use cases
Septra
Communication Devices
(SmartPhone, Walkie-talkie, VOIP, wearables)
Septra
Teleconferencing
Septra
Human to Machine Communication
Demos
Real-Time Noise Suppression over a VOIP call
Key Features
- Edge-based Voice Command recognition engine (No Internet connection required)
- Deep Learning based algorithm
- With Wake-word support
- High recognition rate in noisy environment
- Supports multiword voice commands (1 to 3 words in single command)
- Ultra low memory footprint (<38 KB RAM)
- Very low MCPS (<70 MHz on Cortex M4 CPU)
- Support for multiple languages
Uniqueness
Accuracy
Works well in noisy environments. Coupled with our noise suppression engine, recognition rates higher than 95% are consistently achieved.
Requires Minimal Voice Samples
Our unique audio data preparation technology expands a minimal set of original voice samples to a synthetic dataset that is orders of magnitude larger. This data preparation tool is part of user software and allows infield training.
Enabling “Tiny ML” class of applications
Our AI solutions are designed specifically for low-cost, low-power edge devices built using MCU, DSP and FPGA. With ultra-low memory footprint, customer applications have access to more RAM.
Use cases
Septra
Smart Home
Septra
Automotive
Septra
Industrial equipment
Septra
Speakers & Headphones
Septra
Audio-video devices
Demos
Voice Command Recognition on Renesas MCU
Sound Type Identification
IGN-SEC enables the classification of ambient sound allowing precise identification of various sound types. The underlying algorithms are accurate enough to discriminate between very similar sound types (eg. two different sirens, the bark of two different dog breeds, etc.)
Anomalous Sound Detection:
Anomalies in operation of equipments & infrastructure can be caught early on, by analysing the sounds picked up by microphones installed on or close to the equipment. IGN-SEC then categorizes the picked-up audio as normal or abnormal, allowing early failure prediction of these machines.