Скачать или смотреть 🔊 Real-Time Voice Command LED Control using Python (SVM + MFCC) | Signal Processing Project

📘 Project Title:
Voice-Controlled LED System using Support Vector Machine (SVM) and MFCC Feature Extraction

📚 Subject Context:
This project demonstrates a practical application of Signals, Networks, and Systems (SNS) — a core concept in EXTC engineering.
It transforms speech signals into digital commands to control an LED in real-time using machine learning and signal processing techniques.

🧠 Project Overview

In this project, a user’s voice is captured through a microphone, converted into a digital signal, processed using MFCC (Mel-Frequency Cepstral Coefficients) for feature extraction, and classified using an SVM (Support Vector Machine) model to recognize commands like “ON”, “OFF”, “ONE”, “TWO”, and “THREE”.
Each command instantly changes the LED color, showcasing real-time system response and signal-based control.

⚙️ Technical Highlights

🎧 Signal Acquisition: Microphone input sampled at 22.05 kHz
📈 Pre-processing: RMS and energy-based Voice Activity Detection
🔍 Feature Extraction: MFCC + statistical descriptors (mean, std, min, max)
🧠 Model Used: SVM (RBF Kernel) for multi-class classification
⚡ Real-Time Implementation: Live audio input, instant LED feedback
🖥️ Visualization: LED status displayed dynamically via Matplotlib
🧩 Core SNS Concepts: Signal representation, system response, feature transformation, and classification network

🔬 Key Learning Outcomes

Understanding signal flow and processing stages
Applying frequency-domain analysis (Fourier concepts)
Modeling real-world systems using ML classifiers
Demonstrating system response and stability in real time
💡 Tools and Libraries Used
Python | NumPy | Librosa | scikit-learn | Matplotlib | SoundDevice | Joblib

🎓 Branch / Year / Subject

Department: Electronics and Telecommunication Engineering (EXTC)
Year: Second Year (Semester III / IV)
Subject: Signals, Networks, and Systems

🧾 Chapters

0:00 – Introduction
0:25 – Problem Statement & Objective
1:00 – Signal Processing Concepts
2:15 – Feature Extraction (MFCCs)
3:10 – System Design with SVM
4:00 – Real-Time Demo
5:10 – Visualization Output
5:45 – Conclusion & Future Scope