Quantile based noise estimation for spectral subtraction and Wiener filtering
| Autor: | Rolf Bippus, Alexander Fischer, Volker Stahl |
|---|---|
| Rok vydání: | 2002 |
| Předmět: |
Microphone
Computer science Speech recognition Noise reduction computer.software_genre symbols.namesake Median filter Value noise Audio signal processing Signal processing Noise measurement Noise (signal processing) Wiener filter Wiener deconvolution Salt-and-pepper noise White noise Speech processing Noise floor Sound recording and reproduction Speech enhancement Gradient noise Noise Gaussian noise symbols Algorithm computer |
| Zdroj: | ICASSP |
| DOI: | 10.1109/icassp.2000.862122 |
| Popis: | Elimination of additive noise from a speech signal is a fundamental problem in audio signal processing. In this paper we restrict our considerations to the case where only a single microphone recording of the noisy signal is available. The algorithms which we investigate proceed in two steps. First, the noise power spectrum is estimated. A method based on temporal quantiles in the power spectral domain is proposed and compared with pause detection and recursive averaging. The second step is to eliminate the estimated noise from the observed signal by spectral subtraction or Wiener filtering. The database used in the experiments comprises 6034 utterances of German digits and digit strings by 770 speakers in 10 different cars. Without noise reduction, we obtain an error rate of 11.7%. Quantile based noise estimation and Wiener filtering reduce the error rate to 8.6%. Similar improvements are achieved in an experiment with artificial, non-stationary noise. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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