论文标题
使用自我监督和对抗性培训的端到端音乐重新制作系统
End-to-end Music Remastering System Using Self-supervised and Adversarial Training
论文作者
论文摘要
掌握是音乐制作中的重要一步，但这也是一项具有挑战性的任务，它必须通过经验丰富的音频工程师的手来调整歌曲的音调，空间和音量。重新制作遵循相同的技术过程，其中上下文在于掌握时代的歌曲。由于这些任务具有较高的进入障碍，我们的目标是通过提出端到端的音乐重新制作系统来降低障碍，该系统将输入音频的掌握风格转换为目标。该系统以一种自我监督的方式进行了训练，其中发布的流行歌曲被用于训练。我们还期望该模型通过应用预训练的编码器和投影歧视器来生成现实的音频，以反映参考的主体风格。我们通过定量指标和主观听力测试来验证结果，并表明该模型生成了类似于目标的掌握样式样本。
Mastering is an essential step in music production, but it is also a challenging task that has to go through the hands of experienced audio engineers, where they adjust tone, space, and volume of a song. Remastering follows the same technical process, in which the context lies in mastering a song for the times. As these tasks have high entry barriers, we aim to lower the barriers by proposing an end-to-end music remastering system that transforms the mastering style of input audio to that of the target. The system is trained in a self-supervised manner, in which released pop songs were used for training. We also anticipated the model to generate realistic audio reflecting the reference's mastering style by applying a pre-trained encoder and a projection discriminator. We validate our results with quantitative metrics and a subjective listening test and show that the model generated samples of mastering style similar to the target.
