论文标题
从人工神经网络到音乐发电深度学习 - 历史，概念和趋势
From Artificial Neural Networks to Deep Learning for Music Generation -- History, Concepts and Trends
论文作者
论文摘要
当前的深度学习浪潮（人工神经网络的超空回报）不仅适用于传统的统计机器学习任务：预测和分类（例如，用于天气预测和模式识别），而且已经征服了其他领域，例如翻译。越来越多的应用领域是创意内容的产生，特别是音乐的情况，这是本文的主题。动机是利用现代深度学习技术的能力自动从任意音乐语料库中学习音乐风格，然后从估计的分布中生成音乐样本，并对这一代人进行一定程度的控制。本文提供了基于深度学习技术的音乐生成教程。在对该主题进行了简短的介绍之后，该论文使用人工神经网络进行了音乐发电以及他们的开拓性贡献如何预先设计了当前技术。然后，我们介绍了一些概念框架，以分析所涉及的各种概念和维度。引入和分析了最新系统的各种示例，以说明各种问题和技术的多样性。
The current wave of deep learning (the hyper-vitamined return of artificial neural networks) applies not only to traditional statistical machine learning tasks: prediction and classification (e.g., for weather prediction and pattern recognition), but has already conquered other areas, such as translation. A growing area of application is the generation of creative content, notably the case of music, the topic of this paper. The motivation is in using the capacity of modern deep learning techniques to automatically learn musical styles from arbitrary musical corpora and then to generate musical samples from the estimated distribution, with some degree of control over the generation. This paper provides a tutorial on music generation based on deep learning techniques. After a short introduction to the topic illustrated by a recent exemple, the paper analyzes some early works from the late 1980s using artificial neural networks for music generation and how their pioneering contributions have prefigured current techniques. Then, we introduce some conceptual framework to analyze the various concepts and dimensions involved. Various examples of recent systems are introduced and analyzed to illustrate the variety of concerns and of techniques.
