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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)

Key Features

Deep learning based RNS

Small footprint and low MIPS

All sample rates are supported (eg. 8Khz/16Khz/48Khz)

Works with single MIC

Optimized for high-performance end-to-end voice pipelines


Custom Deep Neural Network

Works for Stationary & non-Stationary noise

Low latency (<25 ms)

Scalable from MCUs to FPGAs to SoCs

Use cases

Mobile, laptop, watches


Communication Devices

(SmartPhone, Walkie-talkie, VOIP, wearables)




Human to Machine Communication

Case Studies

Backside of a head with headset


Real-time Noise Suppression on Edge Devices Using a Practical AI-based Approach


Real-Time Noise Suppression over a VOIP call

Voice Command Engine (IGN-VCE)

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

Neural Network based Voice Processing Workflow



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


Smart Home

Some vehicles




Industrial equipment

Speakers and Headsets


Speakers & Headphones

Mobile, laptop, watches


Audio-video devices


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.


A mechanic workshop


Consumer Electronics



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