Abstract
Deep learning has enabled remarkable advances in style transfer across various domains, offering new possibilities for creative content generation. However, in the realm of symbolic music, generating controllable and expressive performance-level style transfers for complete musical works remains challenging due to limited datasets, especially for genres such as jazz, and the lack of unified models that can handle multiple music generation tasks. This paper presents ImprovNet, a transformer-based architecture that generates expressive and controllable musical improvisations through a self-supervised corruption-refinement training strategy. ImprovNet unifies multiple capabilities within a single model: it can perform cross-genre and intra-genre improvisations, harmonize melodies with genre-specific styles, and execute short prompt continuation and infilling tasks. The model's iterative generation framework allows users to control the degree of style transfer and structural similarity to the original composition. Objective and subjective evaluations demonstrate ImprovNet's effectiveness in generating musically coherent improvisations while maintaining structural relationships with the original pieces. The model outperforms Anticipatory Music Transformer in short continuation and infilling tasks and successfully achieves recognizable genre conversion, with 79\% of participants correctly identifying jazz-style improvisations. Our code and demo page can be found at this https URL.
Abstract (translated)
深度学习已经在多个领域内实现了风格转换的显著进展，为创意内容生成提供了新的可能性。然而，在象征性音乐（如爵士乐等）中，由于数据集有限和缺乏能够处理多种音乐生成任务的统一模型，完整作品的可控且富有表现力的演奏级风格转移仍是一个挑战。本文介绍了ImprovNet，这是一种基于Transformer架构的方法，通过自我监督的破坏-精炼训练策略生成表达性和可控制性的音乐即兴创作。ImprovNet将多项能力整合在一个单一模型中：它可以进行跨流派和同一流派内的即兴演奏、以特定风格和声旋律，并执行短提示延续和填空任务。该模型的迭代生成框架允许用户控制风格转移的程度以及与原始作品结构相似性的程度。客观和主观评估表明，ImprovNet在产生音乐连贯的即兴表演的同时，能够保持与原作结构的关系方面是有效的。在短续写和填空任务中，ImprovNet优于预测性音乐Transformer，并成功实现了可识别的流派转换，有79%的参与者正确辨识了爵士风格的即兴演奏。我们的代码和演示页面可在[此处](https://example.com)找到（注：实际链接请参见原文）。
URL
https://arxiv.org/abs/2502.04522
