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Time delay estimation based on log-sum and l-norm penalized minor component analysis.

Jun-Seok Lim1, Hee-Suk Pang1, Keunhwa Lee2

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This study introduces novel adaptive algorithms for time-delay estimation (TDE) that leverage channel sparsity. These methods enhance source localization accuracy in noisy and reverberant conditions.

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Area of Science:

  • Signal Processing
  • Acoustics
  • Machine Learning

Background:

  • Time-delay estimation (TDE) is crucial for source identification, localization, and tracking.
  • Generalized cross-correlation is a popular TDE method, but adaptive eigenvalue decomposition (EVD) algorithms offer improvements in reverberant environments.

Purpose of the Study:

  • To extend adaptive EVD algorithms for TDE by incorporating transfer channel sparsity.
  • To propose novel estimation algorithms using penalized minor component analysis with excitatory and inhibitory learning rules.

Main Methods:

  • Development of two new TDE algorithms based on log-sum and lp-norm penalized minor component analysis.
  • Utilizing excitatory and inhibitory learning rules within the adaptive EVD framework.
  • Simulation of TDE performance under various noise conditions (uncorrelated, correlated) and reverberation levels.

Main Results:

  • The proposed algorithms demonstrate improved time-delay estimation performance.
  • Enhanced accuracy is observed in the presence of significant noise and reverberation.
  • The sparsity of the transfer channel is effectively utilized for better estimation.

Conclusions:

  • The extended adaptive EVD algorithms offer superior TDE performance compared to existing methods.
  • The proposed sparsity-aware approach is effective for source localization in challenging acoustic environments.
  • This work contributes to advancing TDE techniques for practical applications in acoustics and signal processing.