Deep Learning for MIR Tutorial

TL;DR

This tutorial introduces deep learning techniques like CNNs, RNNs, and Siamese networks for music information retrieval, highlighting their applications and providing resources for further learning.

Contribution

It offers a comprehensive overview of deep learning methods tailored for MIR, including practical insights and a public repository for ongoing reference.

Findings

CNNs are standard for audio retrieval

RNNs excel in onset detection tasks

Siamese networks effectively learn music similarity

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.