arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2001.05266cs.IRcs.LGcs.SDeess.AS
收藏

Deep Learning for MIR Tutorial

Center for Digital Safety & Security(数字安全与安全中心);Musimap;Faculty of Informatics(信息学院);Austrian Institute of Technology(奥地利技术研究所);TU Wien(维也纳技术大学)

Alexander Schindler, Thomas Lidy, Sebastian Böck

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑