Causal Convolutional Encoder Decoder-Based Augmented Kalman Filter for Speech Enhancement

Abstract

Speech enhancement using augmented Kalman filter (AKF) suffers from the biased estimates of the linear prediction coefficients (LPCs) of speech and noise signal in noisy conditions. The existing AKF was particularly designed to enhance the colored noise corrupted speech. In this paper, a causal convolutional encoder decoder (CCED)-based method utilizes the LPC estimates of the AKF for speech enhancement. Specifically, a CCED network is used to estimate the instantaneous noise spectrum for computing the LPCs of noise on a framewise basis. Each noise corrupted speech frame is pre-whitened by a whitening filter, which is constructed with the noise LPCs. The speech LPCs are computed from the pre-whitened speech. The improved speech and noise LPCs enables the AKF to minimize residual noise as well as distortion in the enhanced speech. Objective and subjective testing on NOIZEUS corpus reveal that the enhanced speech produced by the proposed method exhibits higher quality and intelligibility than the benchmark methods in various noise conditions for a wide range of SNR levels.