Leveraging repetition to do audio imputation

Ethan Manilow, Bryan A Pardo

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

2 Scopus citations

Abstract

In this work we propose an imputation method that leverages repeating structures in audio, which are a common element in music. This work is inspired by the REpeating Pattern Extraction Technique (REPET), which is a blind audio source separation algorithm designed to separate repeating 'background' elements from nonrepeating 'foreground' elements. Here, as in REPET, we construct a model of the repeating structures by overlaying frames and calculating a median value for each time-frequency bin within the repeating period. Instead of using this model to do separation, we show how this median model can be used to impute missing time-frequency values. This method requires no pre-Training and can impute in scenarios where missing or corrupt frames span the entire audio spectrum. Human evaluation results show that this method produces higher quality imputation than existing methods in signals with a high amount of repetition.

Original languageEnglish (US)
Title of host publication2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages309-313
Number of pages5
ISBN (Electronic)9781538616321
DOIs
StatePublished - Dec 7 2017
Event2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2017 - New Paltz, United States
Duration: Oct 15 2017Oct 18 2017

Publication series

NameIEEE Workshop on Applications of Signal Processing to Audio and Acoustics
Volume2017-October

Other

Other2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2017
CountryUnited States
CityNew Paltz
Period10/15/1710/18/17

Keywords

  • Audio imputation
  • PLCA
  • REPET
  • repetition

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications

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