Noise Suppression Based on Neurophysiologically-Motivated SNR Estimation for Robust Speech Recognition
Abstract
A novel noise suppression scheme for speech signals is proposed which is based on a neurophysiologically-motivated estimation of the local signal-to-noise ratio (SNR) in different frequency chan(cid:173) nels. For SNR-estimation, the input signal is transformed into so-called Amplitude Modulation Spectrograms (AMS), which rep(cid:173) resent both spectral and temporal characteristics of the respective analysis frame, and which imitate the representation of modula(cid:173) tion frequencies in higher stages of the mammalian auditory sys(cid:173) tem. A neural network is used to analyse AMS patterns generated from noisy speech and estimates the local SNR. Noise suppres(cid:173) sion is achieved by attenuating frequency channels according to their SNR. The noise suppression algorithm is evaluated in speaker(cid:173) independent digit recognition experiments and compared to noise suppression by Spectral Subtraction.
Cite
Text
Tchorz et al. "Noise Suppression Based on Neurophysiologically-Motivated SNR Estimation for Robust Speech Recognition." Neural Information Processing Systems, 2000.Markdown
[Tchorz et al. "Noise Suppression Based on Neurophysiologically-Motivated SNR Estimation for Robust Speech Recognition." Neural Information Processing Systems, 2000.](https://mlanthology.org/neurips/2000/tchorz2000neurips-noise/)BibTeX
@inproceedings{tchorz2000neurips-noise,
title = {{Noise Suppression Based on Neurophysiologically-Motivated SNR Estimation for Robust Speech Recognition}},
author = {Tchorz, Jürgen and Kleinschmidt, Michael and Kollmeier, Birger},
booktitle = {Neural Information Processing Systems},
year = {2000},
pages = {821-827},
url = {https://mlanthology.org/neurips/2000/tchorz2000neurips-noise/}
}