  Most recent studies on deep learning based speech enhancement (SE) focused on
improving denoising performance. However, successful SE applications require
striking a desirable balance between denoising performance and computational
cost in real scenarios. In this study, we propose a novel parameter pruning
(PP) technique, which removes redundant channels in a neural network. In
addition, a parameter quantization (PQ) technique was applied to reduce the
size of a neural network by representing weights with fewer cluster centroids.
Because the techniques are derived based on different concepts, the PP and PQ
can be integrated to provide even more compact SE models. The experimental
results show that the PP and PQ techniques produce a compacted SE model with a
size of only 10.03% compared to that of the original model, resulting in minor
performance losses of 1.43% (from 0.70 to 0.69) for STOI and 3.24% (from 1.85
to 1.79) for PESQ. The promising results suggest that the PP and PQ techniques
can be used in a SE system in devices with limited storage and computation
resources.
