ARTEUS: an Algorithmic Rating Tool for Educating Untrained Singers - Santos, Arthur Nicholas dos; Masiero, Bruno Sanches (Anais do Simpósio Brasileiro de Computação Musical; 2025: Anais do XIX Simpósio Brasileiro de Computação MusicalDO - 10.5753/sbcm.2025.13097, 2025)
This study presents the development of a prototype software system for evaluating karaoke performances using a hybrid scoring methodology that integrates objective and non-intrusive subjective metrics. The proposed framework employs scientifically grounded techniques to assess vocal dynamics, timing, pitch, and timbral similarity, complemented by data-driven emotion recognition to capture the perceptual aspects of the performance. A key feature of the system is a composite scoring mechanism with customizable weightings, which enables flexible adaptation to user preferences in pedagogical contexts or entertainment scenarios. By leveraging artificial intelligence models, the system introduces a novel paradigm in performance evaluation, offering a structured yet extensible approach to quantifying aspects of artistic expression that are traditionally considered subjective. Designed with modularity and future scalability in mind, the framework can be expanded to include user-specific evaluation profiles with potential applications in both professional and recreational settings.