reviews: 
Comments: The manuscript presents an innovative approach to recognizing interference time-series signals using a novel interference signal recognition transformer. The integration of diffusion processes with transformer architectures is a novel contribution to the field of interference signal processing. Overall, the proposed method has excellent anti-interference ability.
Strengths: 1. The application of diffusion models with a Transformer in the context of interference signal recognition has not been fully explored. The proposed model addresses a gap in current methodologies. 
2. A mechanism for electromagnetic signal interference is designed to add an appropriate amount of interference to clean signals. SigFormer combines Transformer with convolution, enabling the model to have excellent local feature extraction capabilities and perform well in global context modeling.   
3. The results are well-presented, with thorough comparisons against existing models. The use of relevant benchmarks is appropriate for evaluating performance. The experimental results show the anti-interference ability of the findings.
Weaknesses: 1. There are several grammatical errors throughout the manuscript.   

For example, in the Abstract section, the sentence "Secondly, diffusion signal denoising module was proposed to denoise the input interference signal." should be revised to "Secondly, a diffusion signal denoising module was proposed to denoise the input interference signal."   

In line 45 of the introduction section, the sentence "Compared with LB methods, FB methods have stronger robustness and effectiveness, but its recognition performance depends on manually designed features and classifiers." should be revised to "Compared with LB methods, FB methods have stronger robustness and effectiveness, but their recognition performance depends on manually designed features and classifiers."  

There are many similar grammatical errors in the article. The author should check them carefully.  

2. Some sentences in the paper are difficult to read and understand. The author should improve his English writing skills.  

For example, in the Abstract section, the sentence "We also use various types of noise to improve its denoising effect on electromagnetic signals." This sentence is not very clear. How can noise improve the denoising effect of the signal?  

In line 81 of the introduction section, the sentence "In this paper, inspired by the powerful denoising ability of diffusion models and considering the interference of a large number of signals in reality, combined with the powerful temporal processing capability of transformers, we propose a novel signal recognition method based on diffusion model and transformer." The sentence is quite long and can be made clearer by breaking it up and improving some phrasing.  

3. Each picture has no caption, which makes it difficult for readers to understand the picture. The method does not present enough details.   

For example, in Figure 1, it is not clear how different types of noise are added to the original signals. In addition, in the Diffusion signal denoising module, does the diffusion process only go through four steps? Is the ellipsis missing?  
In Figure 2, the meaning of some blocks should be explained.   
   
4. There are some problems in the picture. In Figure 1, the image and the legend overlap. And the borderlines are very rough. In the Diffusion Process, Some arrows are missing, and the directions are not clear.  

5. Some mathematical symbols are not explained. For example, "σtz" is not explained. There seems to be something wrong with the derivation from Formula 3 to Formula 4. There should be a more detailed derivation process.  

6. The contributions of this paper are limited. Diffusion and transformers have been proposed for a long time. For Sigformer, transformer and convolution are just simple combinations. The method proposed in this paper seems to be a patchwork of existing techniques.  

7. This paper lacks ablation experiments. Additional experiments are needed to verify the role of each component of Sigformer.
Recommendation: 3
Reviewer confidence: 5
Soundness/correctness: 2
Clarity: 2