A Bird Vocalization Classification Method Based on Bidirectional FBank with Enhanced Robustness

Recent advances in audio signal processing and pattern recognition have made the classification of bird vocalization a focus of bioacoustic research. However, the accurate classification of birdsongs is challenged by environmental noise and the limitations of traditional feature extraction methods. This study proposes the iWAVE-BiFBank method, an innovative approach combining improved wavelet adaptive denoising (iWAVE) and a bidirectional Mel-filter bank (BiFBank) for effective birdsong classification with enhanced robustness. The iWAVE method achieves adaptive optimization using the autocorrelation coefficient and peak-sum-ratio (PSR), overcoming the manual adjustments required with and incompleteness of traditional methods. BiFBank combines FBank and inverse FBank (iFBank) to enhance feature representation. This fusion addresses the shortcomings of FBank and introduces novel transformation methods and filter designs to iFBank, with a focus on high-frequency components. The iWAVE-BiFBank method creates a robust feature set, which can effectively reduce the noise of audio signals and capture both low- and high-frequency information. Experiments were conducted on a dataset of 16 species of birds, and the proposed method was verified with a random forest (RF) classifier. The results show that iWAVE-BiFBank achieves an accuracy of 94.00%, with other indicators, including the F1 score, exceeding 93.00%, outperforming all other tested methods. Overall, the proposed method effectively reduces audio noise, comprehensively captures the characteristics of bird vocalization, and provides improved classification performance.