AI-Driven Innovation

AI Audio Localization for IoT

Hear where sound comes from — on a microcontroller

Proven foundations for modern web products

ASP.NET Core & Web API Angular & SignalR MySQL & SQL Server TensorFlow & Python
Edge AI Audio

Spatial hearing for smart devices

We build compact neural networks that give IoT devices the ability to locate and classify sounds — entirely on-device. From smart speakers that point at the speaker, to industrial sensors that detect bearing faults, our models fit within the tight memory and power budgets of microcontrollers.

<1MB
RAM footprint
<50ms
Inference latency

Capabilities

1

Sound Source Localization (DOA)

Deep-learning models estimate the direction of arrival of sound sources using microphone-array inputs, even in noisy environments.

Direction-of-arrival (DOA) estimation
Multi-source tracking
Robustness to reverberation
Real-time angular resolution

Benefits

  • Sub-degree accuracy in ideal conditions
  • Works with 2–8 mic arrays
  • Latency under 50 ms
2

TensorFlow Lite Micro Inference

Quantized neural networks that run directly on MCUs (Cortex-M, ESP32) with no cloud dependency and minimal RAM.

INT8 quantization
Sub-1 MB model footprints
CMSIS-NN acceleration
Offline, on-device inference

Benefits

  • Under 1 MB RAM footprint
  • No network required
  • Privacy-preserving
3

Acoustic Event Detection

Classify environmental sounds — glass break, alarm, speech, machinery faults — for smart-home and industrial monitoring.

Custom sound-class training
Anomaly & fault detection
Wake-word & keyword spotting
Continuous background listening

Benefits

  • 90%+ classification accuracy
  • Always-on, low-power
  • Custom event taxonomy
4

Beamforming & DSP Pipelines

Pre-processing pipelines — beamforming, noise suppression, echo cancellation — feeding the ML models for robust results.

Delay-and-sum / MVDR beamforming
Adaptive noise suppression
Acoustic echo cancellation
Pre-emphasis & MFCC extraction

Benefits

  • +15 dB SNR improvement
  • Handles reverberant rooms
  • Modular DSP blocks

How We Deliver

1

Acoustic Data Collection

Capture real-world audio from your deployment environment with mic-array hardware.

2

Model Training & Quantization

Train DOA/event models in TensorFlow, then quantize to INT8 for edge inference.

3

Firmware Integration

Port to TFLite Micro with CMSIS-NN, integrated into your RTOS or bare-metal app.

4

Validation & Tuning

Benchmark latency, accuracy, and power consumption on the target MCU.

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Email Us

nikflipflop@gmail.com

Call Us

+380-50-97-38-170

Visit Us

Bannyi Lane, 1
Kharkiv, 61000, Ukraine