Estimation of high-variance vehicular noise

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

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
PublisherSpringer US
Pages221-232
Number of pages12
ISBN (Print)9780387795812
DOIs
StatePublished - Dec 1 2009

Keywords

  • 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

  • Engineering(all)

Fingerprint Dive into the research topics of 'Estimation of high-variance vehicular noise'. Together they form a unique fingerprint.

  • Cite this

    Lee, B., & Hasegawa-Johnson, M. A. (2009). Estimation of high-variance vehicular noise. In In-Vehicle Corpus and Signal Processing for Driver Behavior (pp. 221-232). Springer US. https://doi.org/10.1007/978-0-387-79582-9_18