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Play it again IMuCo! Music Composition to Match your Mood

机译：IMuCo再玩一次！音乐创作与您的心情相符

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Relating sounds to visuals, like photographs, is something humans do subconsciously every day. Deep learning has allowed for several image-related applications, with some focusing on generating labels for images, or synthesize images from a text description. Similarly, it has been employed to create new music scores from existing ones, or add lyrics to a song. In this work, we bring sight and sound together and present IMuCo, an intelligent music composer that creates original music for any given image, taking into consideration what its implied mood is. Our music augmentation and composing methodology attempts to translate image “linguistics” into music “linguistics” without any intermediate natural language translation steps. We propose an encoder-decoder architecture to translate an image into music, first classifying it into one of predefined moods, then generating music to match it. We discuss in detail how we created the training dataset, including several feature engineering decisions in terms of representing music. We also introduce an evaluation classifier framework used for validation and evaluation of the system, and present experimental results of IMuCo’s prototype for two moods: happy and sad. IMuCo can be the core component of a framework that composes the soundtrack for longer video clips, used in advertising, art, and entertainment industries.
机译：人们每天都在潜意识中将声音与照片等视觉效果相关联。深度学习允许使用多种与图像相关的应用程序，其中一些专注于生成图像标签或从文本描述中合成图像。同样，它已被用来从现有的乐谱中创建新的乐谱，或在歌曲中添加歌词。在这项工作中，我们将视觉和声音融合在一起，并提出了智能音乐作曲家IMuCo，它可以为任何给定的图像创建原创音乐，同时考虑其隐含的心情。我们的音乐扩充和作曲方法试图将图像“语言学”转换为音乐“语言学”，而无需任何中间自然语言翻译步骤。我们提出了一种编码器-解码器体系结构，将图像转换为音乐，首先将其分类为预定义的心情中的一种，然后生成与之匹配的音乐。我们将详细讨论如何创建训练数据集，包括在表示音乐方面的多项功能工程决策。我们还介绍了用于系统验证和评估的评估分类器框架，并针对两种情绪（愉快和悲伤）展示了IMuCo原型的实验结果。 IMuCo可以是框架的核心组件，该框架可以为广告，艺术和娱乐行业中使用的较长视频剪辑组成音轨。

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