Speeding learning of personalized audio equalization

Bongjun Kim, Bryan A Pardo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Audio equalizers (EQs) are perhaps the most commonly used tools used in audio production. The SocialEQ project is a web-based personalized audio equalization system that uses an alternative interface paradigm to the standard approach. Here, the user names a desired effect (e.g. Make the sound 'warm') and teaches the tool (e.g. An equalizer) what settings make the sound embody the term. Social EQ typically requires 25 ratings to properly personalize the equalization settings. In this paper, we present three methods to improve the speed of generating personalized items (audio settings) so users can be provided personalized EQ curves after rating a much smaller number of examples. These methods can be adapted to any situation where collaborative filtering is desirable, the end products created for users are unique and comparable to each other, but prior users did not rate the same set of examples as the current user. Methods are tested on a data set of 1635 user sessions.

Original languageEnglish (US)
Title of host publicationProceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014
EditorsCesar Ferri, Guangzhi Qu, Xue-wen Chen, M. Arif Wani, Plamen Angelov, Jian-Huang Lai
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages495-499
Number of pages5
ISBN (Electronic)9781479974153
DOIs
StatePublished - Feb 5 2014
Event2014 13th International Conference on Machine Learning and Applications, ICMLA 2014 - Detroit, United States
Duration: Dec 3 2014Dec 6 2014

Publication series

NameProceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014

Other

Other2014 13th International Conference on Machine Learning and Applications, ICMLA 2014
CountryUnited States
CityDetroit
Period12/3/1412/6/14

Keywords

  • audio equalizer
  • collaborative filtering
  • personalized item
  • transfer learning

ASJC Scopus subject areas

  • Computer Science Applications
  • Human-Computer Interaction

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