In island wetlands， the acoustic environment is complex， with various noise sources such as wind， rain， and ocean waves. To effectively address these interferences in bird song processing and improve the accuracy of species identification， a noise reduction method based on adaptive Kalman filtering with linear predictive coding （A-KF-LPC） is proposed to tackle the issue of noise interference in real-time bird song monitoring under complex acoustic conditions in island wetlands. The A-KF-LPC filter enhances stability by weighted filtering bird song signals， while also suppressing noise and providing precise estimations of uncertain small segments within the acoustic signals， progressively approximating the real scenario. Simulations verify the performance of the A-KF-LPC filter， demonstrating its effectiveness in noise reduction. Experimental results show that under different signal to noise ratios （SNRs）， the A-KF-LPC filtering method is more effective in denoising bird songs compared to traditional Kalman filtering and least mean squares （LMS） adaptive filtering methods. Even under conditions where the signal is fully masked by -10 dB noise， the method can still filter out part of the noise. The A-KF-LPC method proposed in this study holds significant application value in the field of acoustic signal processing， offering an efficient and feasible solution for research on bird species in wetland ecosystems， with potential for broader applications.