Build an ESP32 Offline AI Sound Classifier with Edge Impulse & Visual Alerts

Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.

Imagine a doorbell that recognizes your clap pattern, a security system that alerts on glass breaking, or a pet monitor that detects specific barks—all running offline on a pocket-sized device. This kit turns that vision into a working prototype in an afternoon. You’ll capture real-world audio through a high‑quality INMP441 digital MEMS microphone, process it instantly on an ESP32‑S3, and see results on an OLED display while an LED strip lights up with a unique color for each sound class. No cloud dependency, no latency, and no subscription—just a self‑contained edge AI system you can customize further.

What You'll Build

A self-contained sound classifier that listens through an INMP441 I2S microphone, processes audio in real time on the ESP32‑S3, and displays the detected sound class on a 0.96‑inch OLED screen while illuminating a WS2812B LED strip with a distinct colour per class. You’ll load a pre‑trained Edge Impulse model or train your own on four custom sounds—say, clap, whistle, tap, and speech—directly from the Arduino‑compatible firmware. The entire pipeline, from audio capture to inference, stays on the board, making it a fully offline AI solution you can embed into any project.

What You'll Learn

• Record and preprocess audio samples for machine learning using I2S digital microphones.
• Design an impulse in Edge Impulse, extract MFCC features, and train a neural network classifier.
• Export a TensorFlow Lite model and deploy it onto an ESP32‑S3 with the Arduino framework.
• Interface an OLED display via I2C to show classification results and control addressable LEDs based on the detected sound class.

Kit Contents

Why Buy This Kit Instead of Sourcing Parts Separately

Who This Kit Is For

This kit is ideal for CBSE Class 11–12 students exploring the new AI curriculum, B.Tech ECE/EEE undergraduates prototyping embedded ML assignments, and participants in Smart India Hackathon building low‑power IoT solutions. ATL Tinkering Lab mentors will find it a ready‑to‑use edge AI module that aligns with NEP 2020’s emphasis on hands‑on AI literacy, while hobbyists at IIT, NIT, VIT, and BITS can use it as a launchpad for more complex sound‑aware devices.

Built and Backed by Compoden

Every Compoden kit ships with an AI build companion trained on this exact project — accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.