Improved Broad Learning System for Birdsong Recognition

Abstract

Birds play a vital and indispensable role in biodiversity and environmental conservation. Protecting bird diversity is crucial for maintaining the balance of nature, promoting ecosystem health, and ensuring sustainable development. The Broad Learning System (BLS) exhibits an excellent ability to extract highly discriminative features from raw inputs and construct complex feature representations by combining feature nodes and enhancement nodes, thereby enabling effective recognition and classification of various birdsongs. However, within the BLS, the selection of feature nodes and enhancement nodes assumes critical significance, yet the model lacks the capability to identify high quality network nodes. To address this issue, this paper proposes a novel method that introduces residual blocks and Mutual Similarity Criterion (MSC) layers into BLS to form an improved BLS (RMSC-BLS), which makes it easier for BLS to automatically select optimal features related to output. Experimental results demonstrate the accuracy of the RMSC-BLS model for the three construction features of MFCC, dMFCC, and dsquence is 78.85%, 79.29%, and 92.37%, respectively, which is 4.08%, 4.50%, and 2.38% higher than that of original BLS model. In addition, compared with other models, our RMSC-BLS model shows superior recognition performance, has higher stability and better generalization ability, and provides an effective solution for birdsong recognition.