Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
Book cover

Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2012: Machine Learning and Knowledge Discovery in Databases pp 4Cite as

  1. Home
  2. Machine Learning and Knowledge Discovery in Databases
  3. Conference paper
Machine Learning Methods for Music Discovery and Recommendation

Machine Learning Methods for Music Discovery and Recommendation

  • Douglas Eck20 
  • Conference paper
  • 4500 Accesses

Part of the Lecture Notes in Computer Science book series (LNAI,volume 7523)

Abstract

In this talk I will relate current work at Google in music recommendation to the challenge of automatic music annotation (“autotagging”). I will spend most of the talk looking at (a) signal processing and sparse coding strategies for pulling relevant structure from audio, and (b) training multi-class ranking models in order to build good music similarity spaces. Although I will describe some technical aspects of autotagging and ranking via embedding, the main goal of the talk is to foster a better understanding of the real-world challenges we face in helping users find music they’ll love. To this end I will play a number of audio demos illustrating what we can (and cannot) hope to achieve by working with audio.

Download conference paper PDF

Author information

Authors and Affiliations

  1. Google Research, USA

    Douglas Eck

Authors
  1. Douglas Eck
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Intelligent Systems Laboratory, University of Bristol, Merchant Venturers Building, Woodland Road, BS8 1UB, Bristol, UK

    Peter A. Flach, Tijl De Bie & Nello Cristianini,  & 

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Eck, D. (2012). Machine Learning Methods for Music Discovery and Recommendation. In: Flach, P.A., De Bie, T., Cristianini, N. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2012. Lecture Notes in Computer Science(), vol 7523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33460-3_3

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-33460-3_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33459-7

  • Online ISBN: 978-3-642-33460-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

167.114.118.210

Not affiliated

Springer Nature

© 2023 Springer Nature