论文标题
音乐混合样式转移：一种对比度学习方法，用于解开音频效果
Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio Effects
论文作者
论文摘要
我们提出了一个端到端的音乐混合样式传输系统，该系统将输入多音阶的混合方式转换为参考歌曲的混合方式。这是通过预先训练的编码器实现的，并具有对比度目标，可以从参考音乐录制中提取相关信息。我们所有的模型均以一种有效的数据预处理方法从已经处理过的湿多轨数据集中以自我监督的方式进行培训，从而减轻了获得未加工的干燥数据的数据稀缺性。我们分析了提出的编码器，以了解音频效应的分离能力，并通过客观和主观评估验证其在混合样式转移方面的性能。从结果来看，我们显示了所提出的系统不仅可以转换多站音频的混合样式接近参考，而且在使用音乐源分离模型时，通过混合风格的转移也可以使用混合风格的转换。
We propose an end-to-end music mixing style transfer system that converts the mixing style of an input multitrack to that of a reference song. This is achieved with an encoder pre-trained with a contrastive objective to extract only audio effects related information from a reference music recording. All our models are trained in a self-supervised manner from an already-processed wet multitrack dataset with an effective data preprocessing method that alleviates the data scarcity of obtaining unprocessed dry data. We analyze the proposed encoder for the disentanglement capability of audio effects and also validate its performance for mixing style transfer through both objective and subjective evaluations. From the results, we show the proposed system not only converts the mixing style of multitrack audio close to a reference but is also robust with mixture-wise style transfer upon using a music source separation model.
