Skip to main content

Table 5 The average PESQ scores increment and segSNRs increment with and without postfiltering for the typical DNN-based speech enhancement methods under the assigned types of noise

From: Low-complexity artificial noise suppression methods for deep learning-based speech enhancement algorithms

Objective metrics ΔPESQ ΔsegSNR
Noise type White Babble f16 Factory White Babble f16 Factory
CRN 1.02 0.53 0.71 0.74 6.63 4.58 4.35 4.96
SPP-MMSE 1.08 0.55 0.75 0.75 7.39 5.22 5.00 5.75
SPP-proposed-1 1.16 0.61 0.89 0.81 7.83 5.58 5.69 6.07
SPP-proposed-2 1.16 0.64 0.88 0.85 7.85 5.59 5.59 6.18
SPP-proposed-3 1.17 0.6 0.86 0.81 8.00 5.72 5.68 6.34
DCN 0.83 0.54 0.75 0.68 5.37 4.82 5.17 5.34
SPP-MMSE 0.90 0.56 0.79 0.69 5.77 5.31 5.95 5.99
SPP-proposed-1 0.99 0.61 0.90 0.72 6.48 5.71 6.69 6.32
SPP-proposed-2 0.98 0.65 0.89 0.79 6.44 5.76 6.55 6.41
SPP-proposed-3 1.02 0.59 0.87 0.74 6.52 5.76 6.61 6.56
GRN 1.02 0.6 0.77 0.67 6.64 5.01 5.21 5.57
SPP-MMSE 1.07 0.61 0.8 0.67 6.89 5.28 5.8 5.86
SPP-proposed-1 1.18 0.69 0.90 0.72 7.65 5.77 6.50 6.22
SPP-proposed-2 1.18 0.71 0.88 0.78 7.64 5.81 6.39 6.35
SPP-proposed-3 1.20 0.67 0.89 0.73 7.78 5.86 6.51 6.47
DARCN 1.06 0.71 0.92 0.86 6.42 5.29 5.76 5.76
SPP-MMSE 1.15 0.71 0.95 0.88 6.88 5.72 6.48 6.43
SPP-proposed-1 1.17 0.80 1.02 0.92 7.32 5.98 6.88 6.65
SPP-proposed-2 1.13 0.76 0.95 0.90 6.89 5.59 6.42 6.30
SPP-proposed-3 1.20 0.78 1.01 0.93 7.52 6.12 6.98 6.92