Papers with music generation

3 papers
Music for All: Representational Bias and Cross-Cultural Adaptability of Music Generation Models (2025.findings-naacl)

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Challenge: Existing music generation models are limited in their coverage of the musical genres and cultures of the world.
Approach: They propose to use parametric fine tuning techniques to mitigat the bias in existing music datasets.
Outcome: The proposed models are able to perform well across genres and cultures.
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.
AudioStealer: Extracting Audio Prompts via Shapley Value-Guided Query Search (2026.findings-acl)

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Challenge: prompt stealing is a new form of attack that aims to reconstruct high-value prompts that guide music generation.
Approach: They propose a method to steal music prompts from audio domains using a black-box attack framework.
Outcome: The proposed method recovers prompts with high textual consistency to the ground truth while maintaining strong perceptual similarity to the target recordings.

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