Deep Learning-Based Singing Emotion Recognition and Multi-Style Vocal Generation

Abstract

The singing voice is a dense expressive medium in which melodic contour, phonetic timing, vibrato, timbre, and performance context jointly encode emotion. Recent deep learning systems have improved both singing emotion recognition and vocal generation, but many advances remain fragmented across acoustic modeling, data curation, and deployment engineering. This paper offers a system-level examination of deep learning-based singing emotion recognition and multi-style vocal generation, emphasizing architectural trade-offs, infrastructural constraints, evaluation practice, and governance concerns. Rather than focusing on a single model, the discussion connects self-supervised audio representations, attention-based sequence models, diffusion and autoregressive generative methods, differentiable signal processing, and emotion-annotated corpora. It argues that singing systems face distinctive challenges beyond speech: wide pitch ranges, sustained vowels, score alignment, polyphonic accompaniment, stylistic conventions, and multilingual expressivity. Recognition architectures must separate emotion-relevant vocal cues from production and recording artifacts. Generation architectures must balance fidelity, controllability, latency, and cultural diversity. The paper analyzes data infrastructure and corpus governance, including the need for aligned scores, lyrics, annotations, and licensing. It further examines deployment sustainability, energy costs, robustness gaps, fairness risks, and policy implications such as consent, deepfake misuse, and intellectual property. The conclusion outlines forward-looking research directions for transparent, pluralistic, and socially accountable singing voice systems.