摘要翻译：
在卷积神经网络(CNNs)的推动下，图片之间的“风格转换”最近成为一个非常活跃的研究课题，并迅速成为社交媒体中一项非常流行的技术。本文研究了音频领域中的类似问题：如何将参考音频信号的风格转换为目标音频内容？我们提出了一个灵活的框架，该框架使用声音纹理模型来提取描述参考音频风格的统计信息，然后基于优化的音频纹理合成来修改目标内容。与主流的基于优化的视觉传递方法相比，该过程由目标内容而不是随机噪声初始化，优化损失仅涉及纹理而不是结构。在我们的实验中，这些差异被证明是音频风格转换的关键。为了提取感兴趣的特征，我们研究了不同的体系结构，无论是在其他任务上预先训练的，如在图像风格转移中完成的，还是基于人类听觉系统设计的。对不同类型音频信号的实验结果证实了该方法的可行性。
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英文标题：
《Audio style transfer》
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作者：
Eric Grinstein, Ngoc Duong, Alexey Ozerov, Patrick P\'erez
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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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一级分类：Physics        物理学
二级分类：Classical Physics        经典物理学
分类描述：Newtonian and relativistic dynamics; many particle systems; planetary motions; chaos in classical dynamics. Maxwell's  equations and  dynamics of charged systems and  electromagnetic forces in materials. Vibrating systems such as membranes and cantilevers; optomechanics. Classical waves, including acoustics and elasticity; physics of music and musical instruments. Classical thermodynamics and  heat flow problems.
牛顿和相对论动力学；许多粒子系统；行星运动；经典动力学中的混沌。材料中带电系统和电磁力的麦克斯韦方程组和动力学。振动系统，如薄膜和悬臂；光学力学。经典波，包括声学和弹性；音乐和乐器物理学。经典热力学和热流问题。
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英文摘要：
  'Style transfer' among images has recently emerged as a very active research topic, fuelled by the power of convolution neural networks (CNNs), and has become fast a very popular technology in social media. This paper investigates the analogous problem in the audio domain: How to transfer the style of a reference audio signal to a target audio content? We propose a flexible framework for the task, which uses a sound texture model to extract statistics characterizing the reference audio style, followed by an optimization-based audio texture synthesis to modify the target content. In contrast to mainstream optimization-based visual transfer method, the proposed process is initialized by the target content instead of random noise and the optimized loss is only about texture, not structure. These differences proved key for audio style transfer in our experiments. In order to extract features of interest, we investigate different architectures, whether pre-trained on other tasks, as done in image style transfer, or engineered based on the human auditory system. Experimental results on different types of audio signal confirm the potential of the proposed approach.
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PDF链接：
https://arxiv.org/pdf/1710.11385