Abstract
The article presents the development results of the «tunefinder» web application designed for the intelligent analysis of musical compositions. The system enables users to analyze audio tracks by extract-ing quantitative musical features such as tempo (BPM), key, and rhythmic structure using advanced algo-rithms and neural networks. The solution integrates convolutional-recurrent neural networks (CRNNs), the Krumhansl-Schmuckler algorithm with the Aarden-Essen profile, and the RhythmExtractor2013 from the Essentia library. The architecture is built on a modular and scalable design with a Gradio-based in-terface and RESTful API for efficient data exchange. MongoDB is used for flexible NoSQL data storage. The application supports external integration with Shazam and Spotify to enhance metadata analysis. Op-timization methods such as asynchronous processing, result caching, and server-sent events (SSE) ensure high performance. The service serves musicians, educators, and analysts, providing tools for single-track evaluation, comparative key analysis, and interactive visualizations.