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[电气工程与系统科学] 基于音乐词表示的流行音乐旋律生成 属性 [推广有奖]

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大多数88 在职认证  发表于 2022-3-7 08:19:00 来自手机 |只看作者 |坛友微信交流群|倒序 |AI写论文

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摘要翻译:
流行音乐的自动旋律生成一直是人工智能研究人员和音乐人的一个长期愿望。然而,由于许多因素,学习产生悦耳的旋律是非常具有挑战性的。纸币多元属性的表示一直是纸币研究的主要挑战之一。它也很难保持在允许的音乐多样性范围内,否则将被视为没有听觉愉悦的简单随机的戏剧。观察流行音乐的传统结构提出了进一步的挑战。在本文中,我们建议将每个音符及其性质表示为一个唯一的“单词”,从而减少性质之间不对齐的可能性,并降低学习的复杂性。我们还对音符的范围实施规则化政策,从而鼓励生成的旋律保持接近人类容易遵循的旋律。此外,我们根据歌曲的部分信息生成旋律,从而复制整首歌曲的整体结构。实验结果表明,我们的模型可以生成听觉愉悦的歌曲,与以前的模型相比,更难以区分人类创作的歌曲。
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英文标题:
《Melody Generation for Pop Music via Word Representation of Musical
  Properties》
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作者:
Andrew Shin, Leopold Crestel, Hiroharu Kato, Kuniaki Saito, Katsunori
  Ohnishi, Masataka Yamaguchi, Masahiro Nakawaki, Yoshitaka Ushiku, Tatsuya
  Harada
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最新提交年份:
2017
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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。
--
一级分类:Computer Science        计算机科学
二级分类:Multimedia        多媒体
分类描述:Roughly includes material in ACM Subject Class H.5.1.
大致包括ACM学科类H.5.1中的材料。
--
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:
  Automatic melody generation for pop music has been a long-time aspiration for both AI researchers and musicians. However, learning to generate euphonious melody has turned out to be highly challenging due to a number of factors. Representation of multivariate property of notes has been one of the primary challenges. It is also difficult to remain in the permissible spectrum of musical variety, outside of which would be perceived as a plain random play without auditory pleasantness. Observing the conventional structure of pop music poses further challenges. In this paper, we propose to represent each note and its properties as a unique `word,' thus lessening the prospect of misalignments between the properties, as well as reducing the complexity of learning. We also enforce regularization policies on the range of notes, thus encouraging the generated melody to stay close to what humans would find easy to follow. Furthermore, we generate melody conditioned on song part information, thus replicating the overall structure of a full song. Experimental results demonstrate that our model can generate auditorily pleasant songs that are more indistinguishable from human-written ones than previous models.
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PDF链接:
https://arxiv.org/pdf/1710.11549
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关键词:流行音乐 Presentation Experimental Conventional Applications 降低 整体 研究 范围 流行音乐

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