利用自我注意卷积神经网络实现音乐中的语音和伴奏分离（CS SD）---用户6853689

几十年来，音乐声源分离一直是信号处理领域的一个热门课题，不仅因为其技术难度大，而且由于其在许多商业应用中的重要性，如自动伴音和重混音等。本文提出了一种新颖的自注意网络，将音乐中的声乐与伴奏分离开来。首先，构建一个具有紧密连接的CNN块的卷积神经网络(convolutional neural network, CNN)作为我们的基网络。然后，我们在基础CNN的不同层次插入自我注意子网，以利用音乐的长期内依赖，即重复性。在自我注意子网络中，同样的音乐模式的重复可以重建其他的重复，以获得更好的音源分离性能。结果表明，该方法使声分离的SDR相对提高了19.5%。我们也将我们的方法与先进的MMDenseNet和MMDenseLSTM系统进行了比较。

原文题目：Voice and accompaniment separation in music using self-attention convolutional neural network

原文：Music source separation has been a popular topic in signal processing for decades, not only because of its technical difficulty, but also due to its importance to many commercial applications, such as automatic karoake and remixing. In this work, we propose a novel self-attention network to separate voice and accompaniment in music. First, a convolutional neural network (CNN) with densely-connected CNN blocks is built as our base network. We then insert self-attention subnets at different levels of the base CNN to make use of the long-term intra-dependency of music, i.e., repetition. Within self-attention subnets, repetitions of the same musical patterns inform reconstruction of other repetitions, for better source separation performance. Results show the proposed method leads to 19.5% relative improvement in vocals separation in terms of SDR. We compare our methods with state-of-the-art systems i.e. MMDenseNet and MMDenseLSTM.

原文作者：Yuzhou Liu (1), Balaji Thoshkahna (2), Ali Milani (3), Trausti Kristjansson (3) ((1) Ohio State University (2) Amazon Music, Bangalore (3) Amazon Lab126, CA)

原文地址：https://arxiv.org/abs/2003.08954