Presentation Open Access

Timbral Analysis of Music Audio Signals with Convolutional Neural Networks

Jordi Pons; Olga Slizovskaia; Rong Gong; Emilia Gómez; Xavier Serra


Dublin Core Export

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Jordi Pons</dc:creator>
  <dc:creator>Olga Slizovskaia</dc:creator>
  <dc:creator>Rong Gong</dc:creator>
  <dc:creator>Emilia Gómez</dc:creator>
  <dc:creator>Xavier Serra</dc:creator>
  <dc:date>2017-09-04</dc:date>
  <dc:description>This is the presentation slides for the paper


Timbral Analysis of Music Audio Signals with Convolutional Neural Networks 


This paper has been presented in Eusipco 2017 conference.

The preprint paper PDF can be found here: https://arxiv.org/abs/1703.06697</dc:description>
  <dc:identifier>https://zenodo.org/record/884445</dc:identifier>
  <dc:identifier>10.5281/zenodo.884445</dc:identifier>
  <dc:identifier>oai:zenodo.org:884445</dc:identifier>
  <dc:relation>info:eu-repo/grantAgreement/EC/FP7/267583/</dc:relation>
  <dc:relation>url:https://arxiv.org/pdf/1703.06697.pdf</dc:relation>
  <dc:relation>doi:10.5281/zenodo.884444</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/mdm-dtic-upf</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by-nc/4.0/legalcode</dc:rights>
  <dc:subject>eusipco</dc:subject>
  <dc:subject>presentation slides</dc:subject>
  <dc:title>Timbral Analysis of Music Audio Signals with Convolutional Neural Networks</dc:title>
  <dc:type>info:eu-repo/semantics/lecture</dc:type>
  <dc:type>presentation</dc:type>
</oai_dc:dc>
55
49
views
downloads
All versions This version
Views 5555
Downloads 4949
Data volume 187.7 MB187.7 MB
Unique views 5555
Unique downloads 4949

Share

Cite as