Abstract

In this letter, we propose a frequency and detector pruning approach for reducing the computational complexity associated with loudness estimation. The frequency pruning approach exploits the principles of psychoacoustics such that the total neural activity is preserved. The detector pruning approach evaluates the excitation/loudness patterns at nonuniform sample locations and employs signal interpolation techniques to obtain their corresponding high resolution estimates. Comparative results with the Moore and Glasberg loudness estimation process reveal that the proposed pruning approach for loudness estimation performs consistently well for different types of audio signals with a significant reduction in the computational complexity.

Original languageEnglish (US)
Pages (from-to)997-1000
Number of pages4
JournalIEEE Signal Processing Letters
Volume16
Issue number11
DOIs
StatePublished - 2009

Fingerprint

Pruning
Detector
Detectors
Computational complexity
Computational Complexity
Interpolation
High Resolution
Excitation
Interpolate
Evaluate
Estimate

Keywords

  • Audio coding
  • loudness
  • psychoacoustics
  • speech processing

ASJC Scopus subject areas

  • Signal Processing
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

A Frequency/Detector Pruning Approach for Loudness Estimation. / Krishnamoorthi, Harish; Spanias, Andreas; Berisha, Visar.

In: IEEE Signal Processing Letters, Vol. 16, No. 11, 2009, p. 997-1000.

Research output: Contribution to journalArticle

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AB - In this letter, we propose a frequency and detector pruning approach for reducing the computational complexity associated with loudness estimation. The frequency pruning approach exploits the principles of psychoacoustics such that the total neural activity is preserved. The detector pruning approach evaluates the excitation/loudness patterns at nonuniform sample locations and employs signal interpolation techniques to obtain their corresponding high resolution estimates. Comparative results with the Moore and Glasberg loudness estimation process reveal that the proposed pruning approach for loudness estimation performs consistently well for different types of audio signals with a significant reduction in the computational complexity.

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