基于残差注意力U-Net结构的端到端歌声分离模型

汪斌; 陈宁

doi:10.14135/j.cnki.1006-3080.20200903001

基于残差注意力U-Net结构的端到端歌声分离模型

doi: 10.14135/j.cnki.1006-3080.20200903001

汪斌,
陈宁^,

华东理工大学信息科学与工程学院，上海 200237

基金项目: 国家自然科学基金面上项目(61771196)

详细信息

作者简介:
汪斌：汪　斌(1996—)，女，安徽人，硕士生，主要研究方向为音乐源分离和音频信号处理。E-mail：y45180173@mail.ecust.edu.cn

通讯作者:
陈　宁，E-mail：chenning_750210@163.com

中图分类号: TP391
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出版历程
- 收稿日期: 2020-09-03
- 网络出版日期: 2020-12-16

AN End-to-End Singing Voice Separation Model Based on Residual Attention U-Net

WANG Bin,
CHEN Ning^,

School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China

摘要

摘要: 歌声分离是音乐信息检索领域最具挑战的任务之一，本文对基于Wave-U-Net的歌声分离模型进行了改进以增强其性能。首先，在Wave-U-Net的编码和解码块中设计并引入了残差单元以增强其特征提取的有效性和训练效率；然后，在Wave-U-Net的跳跃连接部分设计并引入了注意力门控机制以减少从编码块对应层提取的特征和来自解码块上一层特征之间的语义鸿沟。在MUSDB18数据集上的实验结果表明：本文提出的RA-WaveUNet模型在分离性能上优于传统的Wave-U-Net模型；采用残差单元和注意力门控机制均有助于提高模型的性能。
- 残差单元 /
- 注意力门控 /
- Wave-U-Net /
- RA-WaveUNet /
- 歌声分离（SVS）
Abstract: The music source separation is to separate a piece of music into its individual sounds. As a specific case, the Singing Voice Separation (SVS) separates the music into vocals and accompaniment. Due to its potential applications in music melody extraction, music genre classification, singing voice detection, and singer identification, etc, SVS has been becoming a hot topic in the music information retrieval field in recent years. It is recently reported that a variety of convolutional neural network architectures based on U-Net has been successfully employed for the SVS task and the better performance can be achieved. Besides, Wave-U-Net was proposed to achieve the end-to-end SVS by analyzing the music waveform directly. However, the performance of the SVS approaches in the time-domain relies heavily on the quality of the feature extraction procedure. In this paper, the conventional Wave-U-Net based SVS scheme is modified for enhancing its performance. Firstly, at the encoding and decoding blocks, a residual unit is designed and adopted to replace the plain neural unit to solve the degradation problem to some extent. Secondly, at the skip connection, an attention gate mechanism is introduced to reduce the semantic gap between the output of the previous layer in the decoding block and the one of the corresponding layer in the encoding block. To verify the effectiveness of the proposed scheme, termed as RA-WaveUNet, in the SVS task, its performances are compared with those of state-of-the-art schemes on the maximum open dataset MUSDB18. It is demonstrated from experimental results that the proposed scheme can achieve better performances than Wave-U-Net based ones and other SVS schemes. Moreover, both the above modifications contribute to the performance enhancement.
- residual unit /
- attention gate /
- Wave-U-Net /
- RA-WaveUNet /
- singing voice separation (SVS)

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图 1 RA-WaveUNet模型框图

Figure 1. Block diagram of RA-WaveUNet model

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图 2 普通神经单位与3种不同残差单元的对比

Figure 2. Comparison between the plain neural unit and three different kinds of residual units

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图 3 注意力门控结构

Figure 3. Architecture of attention gate

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图 4 不同层数M4-R3模型的训练参数数量对比

Figure 4. Parameter numbers comparison of M4-R3 models with different numbers of layers

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图 5 不同层数M4-R3模型的性能对比

Figure 5. Performances achieved by M4-R3 models with different numbers of layers

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表 1 RA-WaveUNet模型结构细节

Table 1. Architecture details of RA-WaveUNet model

Block	Operation	Output shape
	Input	16×384×2
Encoding block $i$,	E-Residual unit $i$	16×240
$i = 1,\cdots,10$	Decimation	16×240
Bridge block	Residual unit 11	16×264
Decoding block $i$,	Linear interpolation	32×264 32×504
Decoding block $i$,	Concat(Att(E-Residual unit $i$))
$i = 10,\cdots,1$	D-Residual unit $i$
	Output	16384×2

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表 2 引入不同类型残差单元后Wave-U-Net的性能对比

Table 2. Performance comparison of Wave-U-Net with different types of residual units

Schemes	Vocals				Accompaniment
Schemes	Med./ dB	MAD/ dB	Mean/ dB	SD/ dB	Med. / dB	MAD/ dB	Mean/ dB	SD/ dB
M4	4.46	3.21	0.65	13.67	10.69	3.15	11.85	7.03
M4-R1	4.63	3.30	1.13	13.11	10.73	3.10	12.21	7.09
M4-R2	4.49	3.15	0.34	14.05	10.47	3.01	11.72	6.77
M4-R3	5.04	3.34	1.43	13.27	10.93	3.09	12.40	6.90

下载: 导出CSV

表 3 BN层对源分离性能的影响

Table 3. Influence of BN layer on the separation performance

Schemes	Vocals				Accompaniment
Schemes	Med. / dB	MAD/ dB	Mean/ dB	SD/ dB	Med. / dB	MAD/ dB	Mean/ dB	SD/ dB
M4	4.46	3.21	0.65	13.67	10.69	3.15	11.85	7.03
M4-R1	4.63	3.30	1.13	13.11	10.73	3.10	12.21	7.09
M4-R1+BN	4.50	3.22	0	14.84	10.56	2.98	11.46	7.16
M4-R2	4.49	3.15	0.34	14.05	10.47	3.01	11.72	6.77
M4-R2+BN	4.38	3.23	−0.54	15.57	10.38	2.93	11.18	6.53
M4-R3	5.04	3.34	1.43	13.27	10.93	3.09	12.40	6.90
M4-R3+BN	4.79	3.31	0.28	14.84	10.85	3.07	11.81	6.64

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表 4 注意力门控机制对性能的影响

Table 4. Contribution of the attention gate to the performance

Schemes	Vocals				Accompaniment
Schemes	Med. / dB	MAD/ dB	Mean/ dB	SD/ dB	Med. / dB	MAD/ dB	Mean/ dB	SD/ dB
M4	4.46	3.21	0.65	13.67	10.69	3.15	11.85	7.03
M4-A	4.52	3.27	0.91	13.29	10.72	3.09	12.03	6.98
M4-R3-10	4.89	3.33	1.28	13.27	10.93	3.09	12.28	6.84
RA-WaveUnet	4.99	3.28	1.54	13.09	10.97	3.09	12.38	6.96

下载: 导出CSV

表 5 与最新SVS模型的性能对比

Table 5. Performance comparison with state-of-the-arts SVS models

Schemes	Vocals				Accompaniment
Schemes	Med. / dB	MAD/ dB	Mean/ dB	SD/ dB	Med. / dB	MAD/ dB	Mean/ dB	SD/ dB
M4	4.46	3.21	0.65	13.67	10.69	3.15	11.85	7.03
MHE0^[13]	4.69	3.24	0.75	13.91	10.88	3.13	12.10	6.77
HydraNet+ H7^[14]	−	−	1.66	4.75	−	−	10.71	2.90
U310^[17]	4.84	3.33	1.09	13.57	10.91	3.14	12.26	6.84
RA-WaveUnet	4.99	3.28	1.54	13.09	10.97	3.09	12.38	6.96

下载: 导出CSV

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