## 5 Conclusions  
In this paper, the compression and reconstruction of bird images and audio are studied. Based on ISTA-Net, a dual attention mechanism is introduced and CBAM_ISTA-Net\({}^{+}\) is proposed. By adding the attention module, the model can capture spatial and channel features more efficiently and improve the reconstruction accuracy of compressed signals. The CBAM_ISTA-Net\({}^{+}\) compression reconstruction experiments are carried out on image and audio spectrogram data of birds. The performance of CBAM_ISTA-Net\({}^{+}\) was compared with ISTA-Net, ISTA-Net\({}^{+}\), and CAE. The experimental results show that CBAM_ISTA-Net\({}^{+}\) achieves a higher PSNR under different compression ratios. In addition, the convolutional neural network (CNN) model is used to classify the original image and the reconstructed image. In the experiments, the classification accuracy of both the original and reconstructed images is high, and that of the reconstructed image is only slightly lower than the original images. This demonstrates that CBAM_ISTA-Net\({}^{+}\) can not only achieve high-quality compressive reconstruction but also preserve the semantic information of images and spectrograms. The original images can be restored with a small amount of sparse signals, thus reducing the cost of data acquisition and storage.  
While CBAM_ISTA-Net\({}^{+}\) has demonstrated a significantly improved performance in compressive reconstruction, it still has some limitations. First, the high computational complexity and time cost of the method in dealing with larger datasets may limit its efficiency in practical applications. Second, although CBAM_ISTA-Net\({}^{+}\) can retain the semantic information of images well, the reconstruction quality may be degraded when dealing with noisy data or high compression rates.  
In the future works, the model structure needs to be further optimized to reduce the computational complexity and improve the efficiency of processing large-scale datasets. Additionally, the research work can be extended to explore methods for compressive sensing in video or other bioacoustic data.  
Conceptualization, D.L. ([PERSON]) and J.L.; Data curation, D.L. ([PERSON]) and Y.F.; Funding acquisition, Y.Z. and D.L. ([PERSON]); Investigation, D.L. ([PERSON]) and J.L.; Methodology, D.L. ([PERSON]), Z.L., and Y.Z.; Supervision, Y.Z. and D.L. ([PERSON]); Validation, Y.F. and D.L. ([PERSON]); Writing--original draft, D.L. ([PERSON]); Writing--review and editing, Y.Z. and D.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
Figure 12: Comparison of classification performance before and after reconstruction.  
Funding: This study was supported by the National Natural Science Foundation of China (Grant No.: 32360388, Grant No.31860332, Grant No.31960142).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data included in this study are available upon request by contact with the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.