Abstract
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 this https URL fidansamet/tune-it-up
Abstract (translated)
深度生成模型已被应用于图像风格转换任务中。在本研究中，我们对CycleGAN模型进行了适应和改进，以实现爵士乐和古典音乐之间的风格转移。通过这种方式，我们的目标是轻松地生成新歌曲、将音乐改编为不同的音乐流派，并减少这些过程中的编曲需求。为了评估风格迁移模型的性能，我们训练并使用了音乐流派分类器。基于此，我们利用多层感知机算法达到了87.7%的准确率。 为了提高我们的风格转移基线，我们在模型中添加了辅助鉴别器和三元组损失函数。根据我们的实验结果，在爵士乐到古典音乐的任务中，我们获得了69.4%的最佳准确性；而在古典音乐到爵士乐的任务中，则是39.3%的最高准确率，这些成绩都是通过使用我们开发的流派分类器得到的。 此外，我们也进行了一项主观实验。该实验的结果表明，我们的转移模型的整体性能良好，并且能够保持输入旋律在转换输出中的完整性。 我们的代码可以在以下链接中找到：https://github.com/fidansamet/tune-it-up 请注意，上述URL是示例性的，在实际使用时需要替换为正确的GitHub地址或相应的开源平台地址。
URL
https://arxiv.org/abs/2503.22008
