Sinusoidal component selection based on partial loudness criteria

Harish Krishnamoorthi, Andreas Spanias

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

1 Scopus citations

Abstract

Sinusoidal models are widely used in parametric speech and audio coding schemes. A common requirement in these applications is to select only a subset of components that provide the greatest perceptual benefit particularly at low bitrates. Usually, perceptual sinusoidal component selection algorithms make use of greedy algorithms that are computationally expensive. In this paper, we present a new algorithm that selects sinusoidal components based on the partial loudness model proposed by Moore & Glasberg. We compare the performance of the proposed algorithm in terms of perceptual benefit and computational complexity to other existing sinusoidal selection algorithms.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages575-579
Number of pages5
DOIs
StatePublished - Oct 18 2013
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: May 26 2013May 31 2013

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
CountryCanada
CityVancouver, BC
Period5/26/135/31/13

Keywords

  • audio coding
  • auditory patterns
  • loudness
  • parametric audio coding
  • sinusoidal models

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Krishnamoorthi, H., & Spanias, A. (2013). Sinusoidal component selection based on partial loudness criteria. In 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings (pp. 575-579). [6637713] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2013.6637713