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Easy Days with Septra®,
a deep learning based Audio AI platform

Voice is the new touch. From industrial machines to consumer appliances, voice is fast becoming a dominant Human Machine Interface. Ignitarium’s Septra platform implements auditory Deep Learning algorithms to deliver ultra-optimized voice and sound analytics solutions on highly constrained edge devices. Let Septra convert your devices into highly attentive listeners.

Real-time Noise Suppression (IGN-RNS)
After
Before

Key Features

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

Voice Command Engine (IGN-VCE)

Key Features

Neural Network based Voice Processing Workflow

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

Some vehicles

Septra

Automotive

Septra

Industrial equipment

Speakers and Headsets

Septra

Speakers & Headphones

Mobile, laptop, watches

Septra

Audio-video devices

Case Studies

An Audio ML based model generator for customized voice commands

Demos

Voice Command Recognition on Renesas MCU

Sound Event Classification (IGN-SEC)

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:

A mechanic workshop

Automotive

Consumer Electronics

Industrial

Surveillance

Edit Content
After
Before

Key Features

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

Edit Content

Key Features

Neural Network based Voice Processing Workflow

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

Some vehicles

Septra

Automotive

Septra

Industrial equipment

Speakers and Headsets

Septra

Speakers & Headphones

Mobile, laptop, watches

Septra

Audio-video devices

Demos

Voice Command Recognition on Renesas MCU

Edit Content

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:

A mechanic workshop

Automotive

Consumer Electronics

Industrial

Surveillance

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