SongEcho：通过实例自适应元素线性调制实现覆盖歌曲生成
SongEcho: Towards Cover Song Generation via Instance-Adaptive Element-wise Linear Modulation
AI总结：
SongEcho通过实例自适应元素线性调制实现覆盖歌曲生成，生成高质量歌曲并减少参数使用。
摘要：
覆盖歌曲是音乐文化的重要组成部分，它在保留原作品核心旋律的基础上，重新诠释以注入新的情感深度和主题强调。尽管先前研究已探索了通过旋律条件化的文本到音乐模型对器乐音乐的重新诠释，但覆盖歌曲生成的任务仍 largely 未被解决。在本工作中，我们将覆盖歌曲生成重新定义为一个条件生成任务，该任务同时在原始人声旋律和文本提示的条件下生成新的人声和伴奏。为此，我们提出了 SongEcho，它利用实例自适应元素线性调制（IA-EiLM），一种通过改进条件注入机制和条件表示来实现可控生成的框架。为了增强条件注入机制，我们将特征线性调制（FiLM）扩展为元素线性调制（EiLM），以促进旋律控制中的精确时间对齐。对于条件表示，我们提出了实例自适应条件细化（IACR），通过与生成模型的隐藏状态交互来细化条件特征，从而产生实例自适应的条件。此外，为了解决大规模、开源全曲数据集稀缺的问题，我们构建了 Suno70k，一个高质量的AI歌曲数据集，其附带有详尽的注解。在多个数据集上的实验结果表明，我们的方法在生成质量上优于现有方法，同时所需的可训练参数少于30%。代码、数据集和演示可在 https://github.com/lsfhuihuiff/SongEcho_ICLR2026 上获取。
英文摘要：
Cover songs constitute a vital aspect of musical culture, preserving the core melody of an original composition while reinterpreting it to infuse novel emotional depth and thematic emphasis. Although prior research has explored the reinterpretation of instrumental music through melody-conditioned text-to-music models, the task of cover song generation remains largely unaddressed. In this work, we reformulate our cover song generation as a conditional generation, which simultaneously generates new vocals and accompaniment conditioned on the original vocal melody and text prompts. To this end, we present SongEcho, which leverages Instance-Adaptive Element-wise Linear Modulation (IA-EiLM), a framework that incorporates controllable generation by improving both conditioning injection mechanism and conditional representation. To enhance the conditioning injection mechanism, we extend Feature-wise Linear Modulation (FiLM) to an Element-wise Linear Modulation (EiLM), to facilitate precise temporal alignment in melody control. For conditional representations, we propose Instance-Adaptive Condition Refinement (IACR), which refines conditioning features by interacting with the hidden states of the generative model, yielding instance-adaptive conditioning. Additionally, to address the scarcity of large-scale, open-source full-song datasets, we construct Suno70k, a high-quality AI song dataset enriched with comprehensive annotations. Experimental results across multiple datasets demonstrate that our approach generates superior cover songs compared to existing methods, while requiring fewer than 30% of the trainable parameters. The code, dataset, and demos are available at https://github.com/lsfhuihuiff/SongEcho_ICLR2026.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
