Real-time acquisition of industrial production data and rapid response to changes in the external environment are key to ensuring the symmetry of a CPS. However, during industrial production, the collected data are inevitably disturbed by environmental noise, which has a huge impact on the subsequent data processing of a CPS. The types of noise vary greatly in different work scenarios in a factory. Meanwhile, barriers such as data privacy protection and copyright restrictions create great difficulties for model synthesis in the information space. A speech enhancement model with teacher–student architecture based on federal knowledge distillation is proposed to alleviate this problem. (1) We pre-train teacher models under different noise conditions to create multiple teacher models with symmetry and excelling in the suppression of a priori noise. (2) We construct a symmetric model–student model of the physical space of the teacher model trained on public data and transfer the knowledge of the teacher model to the student model. The student model can suppress multiple types of noise. Notably, with the TIMIT dataset and the NoiseX92 noise set, the accuracy of the proposed method improved by an average of 1.00% over the randomly specified teacher method in the PESQ metric and 0.17% for STOI.

In this paper, we propose a two-stage heterogeneous lightweight network for monaural speech enhancement. Specifically, we design a novel two-stage framework consisting of a coarse-grained full-band mask estimation stage and a fine-grained low-frequency refinement stage. Instead of using a hand-designed real-valued filter, we use a novel learnable complex-valued rectangular bandwidth (LCRB) filter bank as an extractor of compact features. Furthermore, considering the respective characteristics of the proposed two-stage task, we used a heterogeneous structure, i.e., a U-shaped subnetwork as the backbone of CoarseNet and a single-scale subnetwork as the backbone of FineNet. We conducted experiments on the VoiceBank + DEMAND and DNS datasets to evaluate the proposed approach. The experimental results show that the proposed method outperforms the current state-of-the-art methods, while maintaining relatively small model size and low computational complexity.