Deep Learning has become state of the art in visual computing and continuously emerges into the Music Information Retrieval (MIR) and audio retrieval domain. In order to bring attention to this topic we propose an introductory tutorial on deep learning for MIR. Besides a general introduction to neural networks, the proposed tutorial covers a wide range of MIR relevant deep learning approaches. \textbf{Convolutional Neural Networks} are currently a de-facto standard for deep learning based audio retrieval. \textbf{Recurrent Neural Networks} have proven to be effective in onset detection tasks such as beat or audio-event detection. \textbf{Siamese Networks} have been shown effective in learning audio representations and distance functions specific for music similarity retrieval. We will incorporate both academic and industrial points of view into the tutorial. Accompanying the tutorial, we will create a Github repository for the content presented at the tutorial as well as references to state of the art work and literature for further reading. This repository will remain public after the conference.

中文翻译：

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MIR 深度学习教程

深度学习已成为视觉计算领域的最新技术，并不断涌入音乐信息检索 (MIR) 和音频检索领域。为了引起对这个主题的关注，我们提出了一个关于 MIR 深度学习的介绍性教程。除了对神经网络的一般介绍外，所提议的教程还涵盖了广泛的 MIR 相关深度学习方法。\textbf{卷积神经网络} 目前是基于深度学习的音频检索的事实上的标准。\textbf{循环神经网络} 已被证明在开始检测任务中是有效的，例如节拍或音频事件检测。\textbf{Siamese Networks} 已被证明在学习特定于音乐相似性检索的音频表示和距离函数方面是有效的。我们将把学术和工业的观点纳入教程。伴随本教程，我们将为教程中提供的内容创建一个 Github 存储库，并引用最先进的作品和文献以供进一步阅读。该存储库将在会议结束后保持公开状态。