Deep Learning for MIR Tutorial

• URL: http://arxiv.org/abs/2001.05266v1
• Date: Wed, 15 Jan 2020 12:23:17 GMT
• Title: Deep Learning for MIR Tutorial
• Authors: Alexander Schindler, Thomas Lidy, Sebastian B\"ock
• Abstract summary: The tutorial covers a wide range of MIR relevant deep learning approaches. textbfConvolutional Neural Networks are currently a de-facto standard for deep learning based audio retrieval. textbfSiamese Networks have been shown effective in learning audio representations and distance functions specific for music similarity retrieval.
• Score: 68.8204255655161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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