Microphone system to block extraneous sounds and isolate sound from the direction of gaze
Devices and components
Arduino® UNO™ Q 2 GB
SparkFun MEMS Microphone Breakout - INMP401 (ADMP401)
Software and tools
Python 3
Arduino Applications Lab
Project description
🎧 REAL-TIME ADAPTIVE EDGE AI BEAMFORMING (FROST ALGORITHM)
Live learning: The system requires no prior knowledge of noise statistics; it adapts as soon as a new noise appears.
Rigid constraints: It maintains a constant gain in the direction of interest, so that the lead vocal is never accidentally cut off.
Self-correction: The update mechanism includes a projection stage that corrects digital “drift,” keeping the system stable for hours.
Built for the Edge: It works directly on time domain samples to maintain ultra-low latency, perfect for ARM or DSP hardware. 💻
Pickups (K): 4
FIR Taps (J): 10 to 20 per microphone
Frequency (FS): 16 kHz - 44.1 kHz
Step Size (MU): Controls learning speed 🚀
Output: Normalized Mono WAV file
🔄 Dynamic steering: Move the “focus” to follow a person who is walking.
🧠 Neural Post-Filtering: Using AI to clean up any remaining “ghost” noise.
O. L. Frost III, “An algorithm for processing linearly constrained adaptive arrays,” Proceedings of the IEEE, 1972.
Adaptive audio beamforming with AI
Note: Content and images are from: https://projecthub.arduino.cc/, with some modifications.
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