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Summary

This study introduces an improved power spectrum model (PSM) for estimating auditory filter (AF) shape by incorporating absolute threshold and internal noise. This enhances AF estimation accuracy, especially at low sound levels.

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Area of Science:

  • Auditory perception
  • Psychoacoustics
  • Signal processing

Background:

  • Auditory filter (AF) shape estimation traditionally uses notched-noise (NN) masking and power spectrum models (PSM).
  • Existing methods face challenges in simultaneous and forward masking paradigms.
  • Improvements are needed for accurate AF shape estimation, particularly at low sound levels.

Purpose of the Study:

  • To enhance auditory filter (AF) shape estimation by improving the power spectrum model (PSM).
  • To investigate the impact of incorporating absolute threshold (AT) and level-dependent internal noise into the PSM.
  • To refine AF estimation for normal hearing (NH) listeners across various frequencies and sound levels.

Main Methods:

  • Measured NN thresholds in normal hearing (NH) listeners at 500, 1000, 2000, and 4000 Hz, focusing on low-level maskers.
  • Developed a new PSM incorporating a compressive gammachirp (cGC) filter and three nonfilter parameters.
  • Explicitly represented absolute threshold (AT) and level-dependent internal noise within the PSM.

Main Results:

  • The proposed PSM with cGC filter and nonfilter parameters improved AF estimation accuracy across a wide range of frequencies and levels.
  • A constant detector signal-to-noise ratio () was observed, simplifying postfiltering.
  • Applying the ANSI standard
  • Hearing Level-0dB
  • function for AT improved noise floor distribution for AF estimation.
  • Level-dependent internal noise mitigated nonlinear effects in simultaneous NN masking.

Conclusions:

  • The enhanced PSM provides more accurate auditory filter (AF) shape estimation, particularly at low sound pressure levels near absolute threshold (AT).
  • The model's improved applicability benefits psychoacoustic research and hearing aid signal processing.
  • Explicitly modeling AT and internal noise advances our understanding of auditory processing.