DZCO is a web‑based platform that captures real‑time motion data from smartphones or wearables and optional emotion inputs to generate music recommendations. Its AI‑driven engine maps motion‑emotion vectors to a metadata‑rich catalog and streams tracks via standard APIs such as Spotify or Apple Music, enabling users to discover songs that align with their current activity and mood.
Funding
Funding not disclosed
Founders
Product
Problem
Music discovery platforms rely primarily on passive listening and algorithmic playlists, which can make it difficult for users to find tracks that match their current physical activity or emotional state. This disconnect limits the ability of listeners to experience music in a way that feels integrated with their movement and mood.
Solution
DZCO provides an interactive interface that translates real‑time motion data and user‑reported emotional inputs into dynamic music recommendations. The system captures motion through device sensors (e.g., accelerometer, gyroscope) and combines it with optional biometric or self‑assessment signals to infer the listener’s state. An AI‑driven recommendation engine maps these inputs to a curated catalog, delivering tracks that align with the detected activity and mood. Users interact with the platform via a web‑based UI that visualizes motion patterns and allows on‑the‑fly adjustments to the emotional weighting. The resulting experience blends physical movement with auditory feedback, enabling a more embodied approach to music discovery.
Target Audience
The primary users are music enthusiasts and creators who want a responsive discovery tool that adapts to their physical activity and mood, as well as developers of automotive or fitness applications seeking an embedded music‑interaction layer.
Features
- Sensor integration that captures accelerometer and gyroscope data from smartphones or wearables to quantify user motion
- Optional emotion input module (self‑rating sliders or biometric APIs) that feeds affective data into the recommendation algorithm
- Real‑time recommendation engine that matches motion‑emotion vectors to a metadata‑rich music library using machine‑learning similarity models
- Interactive web dashboard displaying motion trajectories, emotion scores, and currently playing track with playback controls
- Seamless streaming integration via standard APIs (e.g., Spotify, Apple Music) for instant playback of suggested songs
- Customizable mapping profiles allowing users to prioritize activity type, intensity, or emotional dimension
- Low‑latency audio rendering that updates track selection as motion or emotion inputs change