Design and implementation of subspace-based speech enhancement under in-car noisy environments

Chung Hsien Yang, Jia Ching Wang, Jhing Fa Wang, Chung Hsien Wu, Kai Hsing Chang

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


In this paper, a new subspace-based speech enhancement model is presented for in-car speech enhancement. To effectively suppress background noise, this model incorporates a perceptual filterbank and an auditory gain adaptation derived from a psychoacoustic model into a signal subspace approach. The projection approximation subspace tracking deflation (PASTd) algorithm is used to track the signal subspace. For real-time processing, a system-on-a-programmable-chip architecture and a very large scale integration design of the PASTd algorithm are proposed. To realize a pipeline computation, this paper presents a pipelined PASTd architecture without data-dependent hazards. The maximum clock rate is 9.7 MHz, and the typical clock rate, which achieves the real-time requirement, is 4.6 MHz. The corresponding architecture was experimentally verified via an ALTERA EPXA10 development board.

Original languageEnglish
Pages (from-to)1466-1479
Number of pages14
JournalIEEE Transactions on Vehicular Technology
Issue number3
StatePublished - May 2008


  • In-car noise
  • Psychoacoustic model (PAM)
  • Speech enhancement
  • Subspace tracking
  • System-on-a-programmable-chip (SoPC)


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