Easy Days with Septra®,

a deep learning based Audio AI platform

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    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

    Uniqueness

    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

    Septra

    Communication Devices

    (SmartPhone, Walkie-talkie, VOIP, wearables)

    Septra

    Teleconferencing

    Septra

    Human to Machine Communication

    Case Studies

    Backside of a head with headset

    SEPTRA

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

    Demos

    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

    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

    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

    Case Studies