用CycleGAN实现爵士与古典音乐风格转换，保留旋律并提升生成质量。
Tune It Up: Music Genre Transfer and Prediction
- 改进CycleGAN加入辅助判别器和三元组损失，增强风格迁移能力。
- 风格转换后分类准确率达69.4%（爵士转古典）和39.3%（古典转爵士）。
- 主观评价显示生成音乐旋律保持良好，适合音乐创作与风格重制场景。
深度生成模型已被用于图像风格迁移。本文将CycleGAN模型适配并改进，用于爵士与古典音乐风格迁移任务，旨在便捷生成新曲目、转换歌曲风格，并减少编曲工作量。我们训练多层感知机（MLP）作为音乐流派分类器，获得87.7%的分类准确率。为提升基线性能，引入辅助判别器与三元组损失。实验结果显示，改进模型在爵士转古典任务中达到最高69.4%准确率，在古典转爵士任务中达39.3%。主观评估表明，生成音乐整体表现良好，且有效保留输入旋律特征。代码已开源：https://github.com/fidansamet/tune-it-up。
原文摘要 · Abstract (English)
Deep generative models have been used in style transfer tasks for images. In this study, we adapt and improve CycleGAN model to perform music style transfer on Jazz and Classic genres. By doing so, we aim to easily generate new songs, cover music to different music genres and reduce the arrangements needed in those processes. We train and use music genre classifier to assess the performance of the transfer models. To that end, we obtain 87.7% accuracy with Multi-layer Perceptron algorithm. To improve our style transfer baseline, we add auxiliary discriminators and triplet loss to our model. According to our experiments, we obtain the best accuracies as 69.4% in Jazz to Classic task and 39.3% in Classic to Jazz task with our developed genre classifier. We also run a subjective experiment and results of it show that the overall performance of our transfer model is good and it manages to conserve melody of inputs on the transferred outputs. Our code is available at https://github.com/ fidansamet/tune-it-up
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