Real-Time Gesture-Based Audio Control System

This repository documents my implementation of a real-time gesture-based audio control system, inspired by [A Real-Time Gesture-Based Control Framework]. My goal is to build an interactive application that allows users to manipulate sound and music in real time using body movements, leveraging computer vision and machine learning.

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Table of Contents

• Project Motivation
• Reference Paper Summary
• System Overview
• Architecture
• Setup and Installation
• Usage Guide
  • Training Phase
  • Mapping Phase
  • Performance Phase
• Experiments & Evaluation
• Key Learnings & Future Work
• References

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Project Motivation

Traditional audio control interfaces (knobs, sliders, keyboards) can be limiting for performers and interactive installations. Inspired by recent advances in gesture recognition and real-time sound control, I set out to create a system where users can intuitively manipulate music and sound using gestures, making audio interaction more expressive and accessible.

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Reference Paper Summary

The reference paper presents a real-time, human-in-the-loop gesture control framework that adapts audio and music based on human movement via live video input. The system uses computer vision (MediaPipe) for landmark extraction, Max/MSP for multimedia processing, and Python-based machine learning for gesture classification. It enables users to train custom gestures (with as few as 50–80 samples), map them to audio controls, and perform real-time manipulation of sound features such as tempo, pitch, and effects.

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System Overview

Key Features:

• Real-time gesture recognition using MediaPipe
• User-friendly gesture training and mapping workflow
• Low-latency audio control via Pyo (Python audio synthesis library)
• Modular design: Python for ML and audio, OSC for communication
• Supports both performance scenarios (cue-based and continuous control)

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Architecture

Main Components

Component	Role
Pyo	Real-time audio synthesis and manipulation in Python
MediaPipe	Real-time body/hand/face landmark detection
Python	Trains and runs gesture classification models (MLP)
OSC	Real-time bridge between gesture recognition and audio

Data Flow

1. Video Capture: Python/OpenCV captures webcam video.
2. Landmark Extraction: MediaPipe extracts body/hand landmarks.
3. Gesture Classification: Python classifies gestures (training or inference).
4. Audio Control: Recognized gesture directly manipulates Pyo audio parameters in real time.

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Setup and Installation

Prerequisites

• Hardware: Computer with webcam and speakers/headphones
• Software:
  • Python 3.x
  • Python packages: mediapipe, numpy, scikit-learn, python-osc, pyo, opencv-python
  • (Optional) Jupyter Notebook for experiments

Installation Steps

1. Clone this repository.
2. Install Python dependencies:

   pip install mediapipe numpy scikit-learn python-osc pyo opencv-python
   

3. Configure OSC communication if needed.

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Usage Guide

Training Phase

1. Start the Python gesture training script to capture video and extract landmarks.
2. Collect gesture samples:
   • Select a gesture to train (e.g., "raise right hand").
   • System guides you through sample collection with metronome/visual cues.
   • Perform the gesture at each cue; neutral movements fill "other" class.
3. Model Training:
   • Python script trains an MLP classifier on your samples.
   • Review metrics; if accuracy is low, add more samples or corner cases.
   • Save the trained model and scaler for reuse.

Mapping Phase

• In Python, assign each trained gesture to an audio control (e.g., volume up, trigger sound, change effect).
• Choose scenario:
  • Performance cue (e.g., trigger song section)
  • Continuous control (e.g., adjust gain in real time)

Performance Phase

• Start the Python application.
• Perform gestures in front of the webcam.
• System recognizes gestures and triggers mapped audio actions in real time (latency < 200 ms) using Pyo.

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Experiments & Evaluation

• Tested with multiple users and gestures (hand/leg raises, etc.).
• Sample counts: 50–80 per gesture for stable accuracy.
• Model: MLP classifier; achieved 90–95% accuracy for basic gestures.
• Latency: End-to-end system response under 0.2 seconds.
• Scenarios tested:
  • Dance performance cueing (timed gesture triggers)
  • Real-time sound control (volume/pitch/effect adjustment)
• Comparative evaluation: Similar accuracy to MediaPipe’s built-in hand gesture recognition.

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Key Learnings & Future Work

Learnings:

• Modular architecture (Pyo + Python + OSC) enables flexible experimentation.
• User-specific training is crucial for high accuracy; generalized models need diverse data.
• Real-time feedback and low latency are achievable with careful engineering.

Future Work:

• Enhance robustness to lighting/background changes.
• Expand gesture vocabulary and support for more complex gestures.
• Explore integration with other creative coding environments (e.g., Unity, TouchDesigner).
• Investigate adaptive learning and user-independent models.

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References

• [A Real-Time Gesture-Based Control Framework]
• [Real-time Hand Gesture Recognition - GitHub]

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Acknowledgment:
This project is built as an independent implementation inspired by the architecture and methodology of the referenced paper.