[AfA2026-PhysicalAI] - Edge AI enabled Adaptive Microphone system for Beamforming & speaker focusing

Python 3

Arduino App Lab

Arduino® UNO™ Q 2GB

SparkFun MEMS Microphone Breakout - INMP401 (ADMP401)

<html><body><h3>🎧 REAL-TIME EDGE AI ADAPTIVE BEAMFORMING (FROST ALGORITHM)</h3> 🌟 OVERVIEW This project implements a real-time adaptive beamformer using the Frost constrained Least Mean Squares (LMS) algorithm. The system operates directly on live multi-microphone audio streams received via a serial interface, providing high-fidelity spatial filtering at the edge. The System Process: 1. 🎤 Stream: Capture synchronized audio from 4 microphones simultaneously. 2. 🛠️ Filter: Apply adaptive spatial filtering to suppress noise and interference. 3. 🎯 Focus: Preserve sound arriving from a specific "look direction." 4. 🔊 Output: Generate a clean, single-channel enhanced audio signal. 5. 💾 Save: Record the processed output as a `.wav` file for analysis. The Core Philosophy: Let the system continuously learn the acoustic environment and automatically suppress unwanted sound while keeping the desired signal crystal clear. <figure><img src="https://projects.arduinocontent.cc/648c10fa-9083-4cef-986b-b0b9d287f182.jpg"/><figcaption>Connection Diagram</figcaption></figure> 🧠 WHY THE FROST ALGORITHM? Unlike simple "delay-and-sum" beamforming, the Frost algorithm introduces hard linear constraints. This ensures a distortionless response in the desired direction while minimizing noise power from every other angle. Because it doesn't need pre-recorded noise samples, it’s the "gold standard" for real-time assistive listening. 💎 🦻 PRIMARY APPLICATION: ASSISTIVE LISTENING This system is primarily designed to solve the "Cocktail Party Problem" for hearing aid users. People with hearing loss often struggle not because sounds are quiet, but because the signal-to-noise ratio is poor, background chatter and traffic merge into a single "wall of noise." This system acts as a software-defined directional ear. By focusing computationally on the speaker directly in front of the listener and actively cancelling competing noise, it provides clarity in complex environments. Because it adapts in real-time, it can handle sudden sounds (like a passing car) without any manual adjustment from the user. 🏢 ADDITIONAL APPLICATIONS Smart Conference Room Systems 👔 In professional environments, this technology can be integrated into centralized hub units. By utilizing steerable constraints, the system automatically focuses on an active speaker while suppressing localized noise like mechanical keyboard clicks, HVAC hum, and side conversations. This ensures remote participants hear only the speaker, making long virtual meetings much less exhausting. Classroom Lecture Capture 🎓 For large lecture halls, the Frost algorithm allows for high-quality instructor recording without needing a lapel mic. The system captures audio from the podium while treating student background noise as interference to be cancelled. This results in studio-quality recordings perfect for online education and automated transcription. ⚡ ALGORITHM HIGHLIGHTS <ol><li>Live Learning: The system requires no prior knowledge of noise statistics; it adapts as soon as a new noise starts. </li></ol><ol><li>Rigid Constraints: It maintains a constant gain in the direction of interest, so the primary voice is never accidentally muted. </li></ol><ol><li>Self-Correcting: The update mechanism includes a projection step that fixes numerical "drift," keeping the system stable for hours on end. </li></ol><ol><li>Built for the Edge: It operates directly on time-domain samples to keep latency ultra-low, perfect for ARM or DSP hardware. 💻 </li></ol> ⚙️ KEY SYSTEM PARAMETERS <ol><li>Mics (K): 4 </li></ol><ol><li>FIR Taps (J): 10-20 per microphone </li></ol><ol><li>Frequency (FS): 16kHz - 44.1kHz </li></ol><ol><li>Step Size (MU): Controls learning speed 🚀 </li></ol><ol><li>Output: Normalized Mono WAV file </li></ol> 🚀 FUTURE EXTENSIONS <ol><li>🔄 Dynamic Steering: Moving the "focus" to follow a person walking. </li></ol><ol><li>🧠 Neural Post-Filtering: Using AI to clean up any remaining "ghost" noises. </li></ol> Reference: <ol><li>O. L. Frost III, "An Algorithm for Linearly Constrained Adaptive Array Processing," Proceedings of the IEEE, 1972. </li></ol></body></html>

[AfA2026-PhysicalAI] - Edge AI enabled Adaptive Microphone system for Beamforming & speaker focusing

Microphone system to block stray sounds & isolate audio coming from the look direction