Control your music player with hand gestures. Wave, swipe, or show a thumbs up to change tracks, adjust volume, and play/pause music using real-time computer vision and gesture recognition.

Music control with hand gesture recognition using Mediapipe, OpenCV, Tensorflow and PyGame

CVMusikPlayer is a real-time computer vision music player that uses hand gesture recognition to control audio playback. A convolutional neural network (CNN), trained on a custom dataset captured with a global shutter camera, classifies gestures from live video and maps them to music actions.

Gestify: An intuitive gesture recognition system that lets you control Spotify with hand gestures. Using computer vision and AI, Gestify recognizes various hand gestures to manage music playback, volume, and more, bringing touch-free control to your listening experience.

Developed wireless music control system utilizing CNN neural network for accurately classifies hand gestures associated with music commands. Integrated with Python OpenCV for real-time Picture and Video Recognition Activity

The Music player with Voice control and Hand gesture recognition

A computer vision experiment that controls music playback using real-time hand gesture recognition with OpenCV and MediaPipe.

A Spotify-inspired web music player built using HTML, CSS, and JavaScript with hand gesture–based visual recognition for controlling music playback.

Developed wireless music control system utilizing CNN neural network for accurately classifies hand gestures associated with music commands. Integrated with Python OpenCV for real-time Picture and Video Recognition

Utilized a convolutional neural network to identify hand gestures and correlate it with music controls (ie. pause, resume, skip, reverse, etc.) with 99.58% recognition accuracy.

Spotify Hand Gesture Controller – Developed a real-time hand gesture recognition system using Python, OpenCV, and MediaPipe to control music playback. Enabled gesture-based actions like play/pause, volume control, and track navigation. Provided a touch-free, interactive user experience compatible with Windows and macOS.

Gesture Music System is a web app that lets users control the page and play music using hand gestures. Built with HTML, CSS, JavaScript, Flask, and Python, it features gesture recognition, five sections, and 10 mood-based playlists for an interactive experience.

Air DJ is a touchless DJ system using OpenCV and YOLO for real-time hand gesture recognition. Control bass, volume, scratching, looping, and effects with intuitive gestures. Features a dynamic music visualizer and multi-hand detection, offering an interactive, futuristic DJ experience.

Gesture Controlled Virtual Guitar uses computer vision and hand-tracking to let users play guitar through natural gestures like strumming and chord changes. It combines real-time gesture recognition with audio synthesis, making music more interactive, fun, and accessible without a physical instrument.

Built a real-time AI assistant integrating hand-gesture recognition with a Jarvis-style voice system. Implemented gestures (swipe, zoom, single/double click) using OpenCV/MediaPipe and mapped them to system actions. Integrated voice/gesture control to open apps, websites, answer queries, and play YouTube music for seamless hands-free interaction.

The 2D Virtual Piano System leverages hand gesture recognition to enable touch-free musical interaction. It provides gesture-based control, including volume adjustment, multi-octave and instrument switching, and automatic sheet music generation. The system integrates OpenCV, with Google’s MediaPipe framework to achieve real-time hand tracking.

This project enables Spotify playback control using hand gestures via a webcam. Built with Python, OpenCV, MediaPipe, and Spotipy, it supports real-time gesture recognition for controlling music — play/pause, skip tracks, adjust volume, and more. It also features a GUI for manual control and voice feedback using pyttsx3. Ideal for touchless media i

This is a simple application of Convolution Neural Networks combined with background elimination to detect different hand gestures. The goal of the project is to enhance the efficiency and ease of use for input commands. Recognizing gestures as input allows computers to be more accessible for the physically impaired and makes interaction more natural. The project will implement hand gesture recognition with the camera which can be further used as an input in order to perform several tasks like controlling the mouse pointer or to play/pause music, controlling smart home devices etc