AI Audio Localization for IoT
Hear where sound comes from — on a microcontroller
Proven foundations for modern web products
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.
Capabilities
Sound Source Localization (DOA)
Deep-learning models estimate the direction of arrival of sound sources using microphone-array inputs, even in noisy environments.
Benefits
- Sub-degree accuracy in ideal conditions
- Works with 2–8 mic arrays
- Latency under 50 ms
TensorFlow Lite Micro Inference
Quantized neural networks that run directly on MCUs (Cortex-M, ESP32) with no cloud dependency and minimal RAM.
Benefits
- Under 1 MB RAM footprint
- No network required
- Privacy-preserving
Acoustic Event Detection
Classify environmental sounds — glass break, alarm, speech, machinery faults — for smart-home and industrial monitoring.
Benefits
- 90%+ classification accuracy
- Always-on, low-power
- Custom event taxonomy
Beamforming & DSP Pipelines
Pre-processing pipelines — beamforming, noise suppression, echo cancellation — feeding the ML models for robust results.
Benefits
- +15 dB SNR improvement
- Handles reverberant rooms
- Modular DSP blocks
How We Deliver
Acoustic Data Collection
Capture real-world audio from your deployment environment with mic-array hardware.
Model Training & Quantization
Train DOA/event models in TensorFlow, then quantize to INT8 for edge inference.
Firmware Integration
Port to TFLite Micro with CMSIS-NN, integrated into your RTOS or bare-metal app.
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