Over four months, this developer contributed to the Purdue-Artificial-Intelligence-in-Music/Evaluator-code repository, building a cross-platform mobile application for real-time video analysis and data capture. They engineered camera capture and backend integration using React Native and TypeScript, enabling live frame streaming and robust video uploads. Their work included developing native modules in C++ and Swift for video property analysis, integrating OpenCV and FFmpeg for computer vision and video processing, and implementing user authentication with persistent storage. Through careful code refactoring and modular design, they improved maintainability and scalability, delivering a stable foundation for AI-driven music research and collaborative evaluation workflows.
October 2025 performance summary for Purdue-Artificial-Intelligence-in-Music/Evaluator-code focused on delivering a robust, user-friendly video evaluation pipeline and secure access controls. Key work centered on video analysis enhancements with camera integration, and a streamlined user authentication flow to support research collaboration and demos.
October 2025 performance summary for Purdue-Artificial-Intelligence-in-Music/Evaluator-code focused on delivering a robust, user-friendly video evaluation pipeline and secure access controls. Key work centered on video analysis enhancements with camera integration, and a streamlined user authentication flow to support research collaboration and demos.
September 2025 monthly summary for Purdue-Artificial-Intelligence-in-Music/Evaluator-code: Focused on delivering end-to-end CV and media-processing improvements, performance optimizations, and code hygiene, delivering business value through enhanced capabilities and maintainability.
September 2025 monthly summary for Purdue-Artificial-Intelligence-in-Music/Evaluator-code: Focused on delivering end-to-end CV and media-processing improvements, performance optimizations, and code hygiene, delivering business value through enhanced capabilities and maintainability.
August 2025 monthly summary for Purdue-Artificial-Intelligence-in-Music/Evaluator-code. Delivered a native video analysis module with platform-specific implementations (Android and iOS), TypeScript definitions, and integration into the main application. The module enables analyzing video properties such as duration and dimensions, establishing a scalable foundation for cross-platform media analytics and future feature development.
August 2025 monthly summary for Purdue-Artificial-Intelligence-in-Music/Evaluator-code. Delivered a native video analysis module with platform-specific implementations (Android and iOS), TypeScript definitions, and integration into the main application. The module enables analyzing video properties such as duration and dimensions, establishing a scalable foundation for cross-platform media analytics and future feature development.
In July 2025, delivered foundational features and real-time capabilities for the Evaluator-code repository, establishing a solid base for ongoing AI-in-Music experiments and scalable data capture. Key outcomes include mobile camera capture with backend integration, real-time frame streaming via WebSockets, robust video upload flow, and enhanced point/line processing. These workstreams enabled faster data collection, improved data reliability, and live analytics feedback for iteration and validation.
In July 2025, delivered foundational features and real-time capabilities for the Evaluator-code repository, establishing a solid base for ongoing AI-in-Music experiments and scalable data capture. Key outcomes include mobile camera capture with backend integration, real-time frame streaming via WebSockets, robust video upload flow, and enhanced point/line processing. These workstreams enabled faster data collection, improved data reliability, and live analytics feedback for iteration and validation.
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