Estimation of high-variance vehicular noise

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this chapter, we describe a method of minimum mean-squared error (MMSE) a posteriori estimation of high-variance vehicular noise. The proposed method considers spectral instances of noise as sampled values from a stochastic noise process and estimates them with given statistical properties of noise and current noisy observation. Accuracy of the noise estimation method is evaluated in terms of the accuracy of a spectrum-based voice activity detection (VAD), in which speech presence is determined by the a priori and a posteriori signal-to-noise ratios (SNRs) in each frequency bin. VAD experiments are performed on clean speech data by adding four different types of vehicular noise, each with the SNR varying from -10 to 20 dB. Also, isolated digit recognition experiments are performed using original noisy recordings from the AVICAR corpus. Experimental results show that the proposed noise estimation method outperforms both the MMSE a priori noise estimation and autoregressive noise adaptation methods especially for low SNR.

Original languageEnglish (US)
Title of host publicationIn-Vehicle Corpus and Signal Processing for Driver Behavior
Number of pages12
ISBN (Print)9780387795812
StatePublished - 2009
Event3rd Biennial Workshop on Digital Signal Processing for Mobile and Vehicular Systems, DSP 2007 - Istanbul, Turkey
Duration: Jun 1 2007Jun 1 2007

Publication series

NameIn-Vehicle Corpus and Signal Processing for Driver Behavior


Other3rd Biennial Workshop on Digital Signal Processing for Mobile and Vehicular Systems, DSP 2007


  • Automatic speech recognition
  • Autoregressive adaptation
  • Error propagation
  • Hidden Markov model
  • Minimum mean-squared error estimation
  • Noise spectrum estimation
  • Periodogram
  • Short-time Fourier transform
  • Speech enhancement
  • Voice activity detection
  • Word error rate

ASJC Scopus subject areas

  • Radiation
  • Engineering(all)


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