Jürgen Tchorz, Michael Kleinschmidt, Birger Kollmeier
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.