摘要翻译：
在音乐领域，特征学习主要有两种方式：基于稀疏表示的无监督学习和基于音乐体裁等语义标签的监督学习。然而，以无监督的方式寻找鉴别特征是一个挑战，使用语义标签的有监督特征学习可能涉及噪声或昂贵的注释。在本文中，我们提出了一种有监督的特征学习方法，使用每个曲目中标注的艺术家标签作为目标元数据。我们提出了两个深度卷积神经网络(DCNN)来学习深度艺术家特征。一个是用整个艺术家标签同时训练的普通DCNN，另一个是用基于艺术家身份的艺术家标签子集训练的暹罗DCNN。我们将训练好的模型应用到迁移学习环境下的音乐分类和检索任务中。结果表明，我们的方法可以与以前的最先进的方法相媲美，表明所提出的方法与使用语义标签学习的模型一样能捕捉到通用的音乐音频特征。同时，我们还讨论了这两种模型的优缺点。
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英文标题：
《Representation Learning of Music Using Artist Labels》
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作者：
Jiyoung Park, Jongpil Lee, Jangyeon Park, Jung-Woo Ha, Juhan Nam
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最新提交年份：
2018
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Sound        声音
分类描述：Covers all aspects of computing with sound, and sound as an information channel. Includes models of sound, analysis and synthesis, audio user interfaces, sonification of data, computer music, and sound signal processing. Includes ACM Subject Class H.5.5, and intersects with H.1.2, H.5.1, H.5.2, I.2.7, I.5.4, I.6.3, J.5, K.4.2.
涵盖了声音计算的各个方面，以及声音作为一种信息通道。包括声音模型、分析和合成、音频用户界面、数据的可听化、计算机音乐和声音信号处理。包括ACM学科类H.5.5，并与H.1.2、H.5.1、H.5.2、I.2.7、I.5.4、I.6.3、J.5、K.4.2交叉。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Audio and Speech Processing        音频和语音处理
分类描述：Theory and methods for processing signals representing audio, speech, and language, and their applications. This includes analysis, synthesis, enhancement, transformation, classification and interpretation of such signals as well as the design, development, and evaluation of associated signal processing systems. Machine learning and pattern analysis applied to any of the above areas is also welcome.  Specific topics of interest include: auditory modeling and hearing aids; acoustic beamforming and source localization; classification of acoustic scenes; speaker separation; active noise control and echo cancellation; enhancement; de-reverberation; bioacoustics; music signals analysis, synthesis and modification; music information retrieval;  audio for multimedia and joint audio-video processing; spoken and written language modeling, segmentation, tagging, parsing, understanding, and translation; text mining; speech production, perception, and psychoacoustics; speech analysis, synthesis, and perceptual modeling and coding; robust speech recognition; speaker recognition and characterization; deep learning, online learning, and graphical models applied to speech, audio, and language signals; and implementation aspects ranging from system architecture to fast algorithms.
处理代表音频、语音和语言的信号的理论和方法及其应用。这包括分析、合成、增强、转换、分类和解释这些信号，以及相关信号处理系统的设计、开发和评估。机器学习和模式分析应用于上述任何领域也是受欢迎的。感兴趣的具体主题包括：听觉建模和助听器；声波束形成与声源定位；声场景分类；说话人分离；有源噪声控制和回声消除；增强；去混响；生物声学；音乐信号的分析、合成与修饰；音乐信息检索；多媒体音频和联合音视频处理；口语和书面语建模、切分、标注、句法分析、理解和翻译；文本挖掘；言语产生、感知和心理声学；语音分析、合成、感知建模和编码；鲁棒语音识别；说话人识别与特征描述；应用于语音、音频和语言信号的深度学习、在线学习和图形模型；以及从系统架构到快速算法的实现方面。
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英文摘要：
  In music domain, feature learning has been conducted mainly in two ways: unsupervised learning based on sparse representations or supervised learning by semantic labels such as music genre. However, finding discriminative features in an unsupervised way is challenging and supervised feature learning using semantic labels may involve noisy or expensive annotation. In this paper, we present a supervised feature learning approach using artist labels annotated in every single track as objective meta data. We propose two deep convolutional neural networks (DCNN) to learn the deep artist features. One is a plain DCNN trained with the whole artist labels simultaneously, and the other is a Siamese DCNN trained with a subset of the artist labels based on the artist identity. We apply the trained models to music classification and retrieval tasks in transfer learning settings. The results show that our approach is comparable to previous state-of-the-art methods, indicating that the proposed approach captures general music audio features as much as the models learned with semantic labels. Also, we discuss the advantages and disadvantages of the two models.
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PDF链接：
https://arxiv.org/pdf/1710.06648