Subband kaiman filtering for speech enhancement

Wen-Rong Wu*, Po Cheng Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

45 Scopus citations


Kaiman filtering is an effective speech-enhancement technique, in which speech signals are usually modeled as autoregressive (AR) processes and represented in the state-space domain. Since AR coefficients identification and Kaiman filtering require extensive computations, real-time implementation of this approach is difficult. This paper proposes a simple and practical scheme that overcomes these obstacles. Speech signals are first decomposed into subbands. Subband speech signals are then modeled as low-order AR processes, such that low-order Kaiman filters can be applied. Enhanced fullband speech signals are finally obtained by combining the enhanced subband speech signals. To identify AR coefficients, predictionor filters adapted by the LMS algorithm are applied. Due to noisy inputs, the LMS algorithm converges to biased solutions. The performance of the Kaiman filter with biased parameters is analyzed. It is shown that accurate estimates of AR coefficients are not required when the driving-noise variance is properly estimated. New methods for making such estimates are proposed. Thus, we can tolerate biased AR coefficients and take advantage of the LMS algorithm's simple structure. Simulation results show that speech enhancement in the subband domain not only greatly reduces the computational complexity, but also achieves better performance compared to that in the fullband domain.

Original languageEnglish
Article number718814
Pages (from-to)1072-1083
Number of pages12
JournalIEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing
Issue number8
StatePublished - Aug 1998


  • Ar modeling
  • Kaiman filtering
  • Lms algorithm
  • Speech enhancement
  • Subband filtering

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