Processing of Vocal Music Using Artificial Intelligence: Unveiling Creative Potential and Shaping Listener Perception

Technological transformations occurring at the intersection of artificial intelligence and vocal music necessitate an epistemological reevaluation of both perceptual mechanisms and creative capacities. The integration of artificial intelligence with vocal music compels a reconsideration of perceptual mechanisms and creative potential. While prior research has focused primarily on the technical accuracy of AI systems, their influence on perception and broader cultural implications has remained largely unexplored. The present study addresses this research gap. This study quantitatively assesses the impact of AI on vocal-music processing. Employing a mixed-methods approach, it examines algorithmic integration within musical traditions, explores correlations between technical parameters and affective responses that characterize human-AI collaboration, and compares architectural variations among systems along with their perceptual consequences. Statistical analysis revealed differences in genre receptivity: contemporary music (EDM and jazz) received a mean score of μ = 9.0, whereas traditional genres such as opera (μ = 5.0) and classical music (μ = 6.0) remained at comparatively low levels. The integration of artificial intelligence enhanced musicians' creative output from M = 6.7 to M = 8.1 and increased audience engagement by 12%. Correlational analysis confirmed a strong association between emotional intensity and listener engagement (r = 0.72). Educational institutions may implement AI-enhanced vocal training programs; recording studios are adopting system-specific technologies tailored to desired affective outcomes; and cultural-heritage initiatives can leverage algorithmic assistance while preserving interpretive authenticity.