  Speech enhancement aims to improve speech quality and intelligibility,
especially in noisy environments where background noise degrades speech
signals. Currently, deep learning methods achieve great success in speech
enhancement, e.g. the representative convolutional recurrent neural network
(CRN) and its variants. However, CRN typically employs consecutive downsampling
and upsampling convolution for frequency modeling, which destroys the inherent
structure of the signal over frequency. Additionally, convolutional layers
lacks of temporal modelling abilities. To address these issues, we propose an
innovative module combing a State space model and Inplace Convolution (SIC),
and to replace the conventional convolution in CRN, called SICRN. Specifically,
a dual-path multidimensional State space model captures the global frequencies
dependency and long-term temporal dependencies. Meanwhile, the 2D-inplace
convolution is used to capture the local structure, which abandons the
downsampling and upsampling. Systematic evaluations on the public INTERSPEECH
2020 DNS challenge dataset demonstrate SICRN's efficacy. Compared to strong
baselines, SICRN achieves performance close to state-of-the-art while having
advantages in model parameters, computations, and algorithmic delay. The
proposed SICRN shows great promise for improved speech enhancement.
