Challenge: Existing methods for song generation fail to generate vocals with prompt-based control and proper alignment.
Approach: VersBand is a multi-task song generation framework for synthesizing high-quality songs with prompt-based control.
Outcome: Experimental results show that VersBand performs better than baseline models across multiple song generation tasks.

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Text-to-Song: Towards Controllable Music Generation Incorporating Vocal and Accompaniment (2024.acl-long)

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Challenge: Existing studies focus on singing voice synthesis and music generation independently.
Approach: They propose a novel task called Text-to-Song synthesis which incorporates both vocal and accompaniment generation.
Outcome: The proposed method can synthesize songs with comparable quality and style consistency.
Unsupervised Melody-to-Lyrics Generation (2023.acl-long)

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Challenge: Existing methods for automatic melody-to-lyric generation are limited due to the limited amount of melody-lyrical aligned data.
Approach: They propose a method for automatic melody-to-lyric generation without training on any aligned melody-lyr data.
Outcome: The proposed model generates high-quality lyrics that are singable, intelligible, and coherent than baseline models.
SegTune: Structured and Fine-Grained Control for Song Generation (2026.acl-long)

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Challenge: Recent advances in neural song generation have enabled high-quality synthesis from lyrics and global textual prompts.
Approach: They propose a framework that allows users to specify local musical descriptions aligned to song segments.
Outcome: The proposed framework outperforms baselines in musicality and controllability.
Prompt-Singer: Controllable Singing-Voice-Synthesis with Natural Language Prompt (2024.naacl-long)

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Challenge: Recent singing-voice-synthesis methods lack ability to control style attributes of synthesized singing.
Approach: They propose a singing-voice-synthesis method that enables attribute controlling on singer gender, vocal range and volume with natural language.
Outcome: The proposed method achieves favorable control ability and audio quality.
Generative Music Models’ Alignment with Professional and Amateur Users’ Expectations (2025.findings-acl)

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Challenge: Recent years have witnessed rapid advances in text-to-music generation using large language models.
Approach: They propose a task to align AI-generated music with human expressions . they use a dataset of over 1.5 million songs to analyze their content .
Outcome: The proposed framework outperforms baseline models and facilitates end-to-end generation of songs audio.
SongComposer: A Large Language Model for Lyric and Melody Generation in Song Composition (2025.acl-long)

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Challenge: Creating lyrics and melodies in symbolic format requires expert knowledge of melody and an advanced understanding of lyrics.
Approach: They introduce SongComposer, a music-specialized large language model that can create symbolic lyrics and melodies following instructions.
Outcome: The proposed model outperforms existing models in symbolic song composition tasks.
TCSinger 2: Customizable Multilingual Zero-shot Singing Voice Synthesis (2025.findings-acl)

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Challenge: Existing zero-shot singing voice synthesis models depend on phoneme and note boundary annotations, limiting their robustness and producing poor transitions between phonemes and notes.
Approach: They propose a multi-task multilingual zero-shot SVS model with style transfer and style control based on various prompts.
Outcome: Experimental results show that TCSinger 2 outperforms baseline models in subjective and objective metrics across multiple related tasks.
VocalRep: Structure-Aware Vocal Representations for Multimodal Generation (2026.findings-acl)

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Challenge: Existing approaches to vocal separation are optimized for signal-level reconstruction, but they overlook structural disentanglement required for downstream generation tasks.
Approach: They propose a structure-aware learning framework to disentangle vocals, harmonies, and accompaniment . they combine global vocal identity conditioning with ranking-based objectives .
Outcome: The proposed framework disentangles lead vocals, harmonies, and accompaniment while enforcing role consistency across long-form audio.
UniLG: A Unified Structure-aware Framework for Lyrics Generation (2023.acl-long)

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Challenge: Existing works ignore musical attributes hidden behind lyrics and structure of lyrics . existing works ignore structure of generated lyrics and do not consider structure of songs .
Approach: They propose a framework for conditional lyrics generation that considers structure and relationship between lyrics and music.
Outcome: The proposed framework improves the structure modeling and unifies different conditions for different types of lyrics generation.
SongRewriter: A Chinese Song Rewriting System with Controllable Content and Rhyme Scheme (2023.findings-acl)

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Challenge: Existing methods of generating singable lyrics are based on a given melody, but there are two main challenges: generating the lyrics without knowing the melody and composing compatible melodies.
Approach: They propose a Chinese lyric generation and editing system which rewrites lyrics of an existing song such that they are compatible with the rhythm of the existing melody.
Outcome: The proposed system is based on a randomized multi-level masking strategy and can generate new lyrics or edit fragments without prior knowledge of melody composition.

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