Speech Enhancement Using Harmonic Emphasis and Adaptive Comb Filtering

An enhancement method for single-channel speech degraded by additive noise is proposed. A spectral weighting function is derived by constrained optimization to suppress noise in the frequency domain. Two design parameters are included in the suppression gain, namely, the frequency-dependent noise-flooring parameter (FDNFP) and the gain factor. The FDNFP controls the level of admissible residual noise in the enhanced speech. Enhanced harmonic structures are incorporated into the FDNFP by time-domain processing of the linear prediction residuals of voiced speech. Further enhancement of the harmonics is achieved by adaptive comb filtering derived using the gain factor with a peak-picking algorithm. The performance of the enhancement method was evaluated by the modified bark spectral distance (MBSD), ITU-Perceptual Evaluation of Speech Quality (PESQ) scores, composite objective measures and listening tests. Experimental results indicate that the proposed method outperforms spectral subtraction; a main signal subspace method applicable to both white and colored noise conditions and a perceptually based enhancement method with a constant noise-flooring parameter, particularly at lower signal-to-noise ratio conditions. Our listening test indicated that 16 listeners on average preferred the proposed approach over any of the other three approaches about 73% of the time.