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A one-dimensional search method with stable 1-norm solution for linear prediction.

M K Jayesh1, C S Ramalingam1

  • 1Department of Electrical Engineering, IIT Madras, Chennai 600036, India jayeshmkoroth@gmail.com, csr@ee.iitm.ac.in.

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Summary

A new iterative algorithm minimizes linear prediction error for stable all-pole filters. This method offers near-optimal performance for vocal tract estimation without complex linear programming.

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Area of Science:

  • Signal Processing
  • Digital Filter Design
  • Speech Analysis

Background:

  • Linear prediction is crucial for modeling signals like speech.
  • Minimizing prediction error is key to accurate filter design.
  • Existing methods can be computationally intensive or lack flexibility.

Purpose of the Study:

  • To propose a simple, stable iterative algorithm for all-pole filter design.
  • To minimize the 1-norm of the linear prediction error signal.
  • To apply the algorithm to vocal tract estimation.

Main Methods:

  • An iterative algorithm minimizing the 1-norm of the linear prediction error.
  • The method supports both autocorrelation and covariance frameworks.
  • It employs a one-dimensional search, avoiding linear programming.

Main Results:

  • The algorithm guarantees a stable all-pole filter.
  • Performance is near-optimal, comparable to interior point methods.
  • The method successfully constrained peak bandwidths.

Conclusions:

  • The proposed iterative algorithm is efficient and effective for all-pole filter design.
  • It provides a robust alternative to existing methods, particularly for vocal tract modeling.
  • The algorithm demonstrates strong performance in spectral distortion metrics for synthetic and natural speech.