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Updated: May 11, 2026

An Introduction to Processing, Fitting, and Interpreting Transient Absorption Data
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An Introduction to Processing, Fitting, and Interpreting Transient Absorption Data

Published on: February 16, 2024

Spectral-envelope-group-delay models for transients.

Ravi R Shenoy1, Chandra Sekhar Seelamantula

  • 1Department of Electrical Engineering, Indian Institute of Science, C. V. Raman Road, Bangalore 560012, India.

The Journal of the Acoustical Society of America
|May 10, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a spectral-domain Amplitude Modulation-Frequency Modulation (AM-FM) model for analyzing transient audio signals. The novel approach effectively models spectral structures, enabling applications in sound analysis and speech processing.

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Area of Science:

  • Signal Processing
  • Acoustics
  • Speech Science

Background:

  • Transient signals lack clear time-domain periodicity.
  • These signals exhibit distinct modulation patterns in the spectral domain due to their brief duration.
  • Existing models may not fully capture the spectral characteristics of transients.

Purpose of the Study:

  • To propose a novel spectral-domain Amplitude Modulation-Frequency Modulation (AM-FM) model for transient signals.
  • To establish a method for estimating spectral envelope (SE) and group delay (GD) from spectral zero-crossings.
  • To evaluate the model's performance in applications like sound modeling, compression, and speech analysis.

Main Methods:

  • Development of a spectral AM-FM model based on real spectral zero-crossings.
  • Estimation of Group Delay (GD) using local polynomial regression on spectral zeros, ensuring spectral continuity.
  • Estimation of Spectral Envelope (SE) from the phase function derived from the estimated GD.

Main Results:

  • The proposed model successfully captures the spectral modulation structure of transient signals.
  • Simulation results demonstrate the noise-robustness of the Spectral Envelope and Group Delay (SEGD) model.
  • The model shows promise in modeling Castanet sounds, transient signal compression, and identifying glottal closure instants in speech.

Conclusions:

  • The spectral AM-FM model provides an effective framework for analyzing transient signals.
  • The SEGD estimation method offers controllable smoothness and robust performance.
  • The model has significant potential for diverse audio and speech processing applications.