# A Bird Vocalization Classification Method Based on Bidirectional FBank with Enhanced Robustness
## 1 Introduction  
Birds play a critical role in biological monitoring and ecological conservation, aiding biologists in formulating biodiversity preservation strategies and providing ecologists with crucial information on the regional behaviors of birds. Consequently, the identification of bird vocalizations has become a key focus in bioacoustics. The traditional classification process consists of pre-processing, feature extraction, and classification [1]. Among these steps, effective denoising in pre-processing is essential for achieving accurate classification [2]. The noise contained in birdsong mainly comes from environmental sources [3], such as wind, rain, and even the buzzing of insects. This kind of noise is typically modeled as additive white Gaussian noise (AWGN) [4, 5], a typical non-stationary oscillation signal. Wavelet transform (WT)-based denoising is regarded as the most effective approach for handling oscillatory signals. It addresses the time-domain resolution limitations of theshort-time Fourier transform (STFT) [6]. Many studies have demonstrated that WT outperforms other techniques, such as the Fourier transform (FT) and discrete cosine transform (DCT) [7; 8; 9]. The wavelet concept was first introduced by [PERSON] [10] in 1989, and it led to another revolution in the field of computer vision, offering advantages in terms of sparsity and multiples resolutions [11]. This study focuses on wavelet threshold denoising, which is essentially the discrete wavelet transform (DWT). The primary factors affecting its effect are the selection of the wavelet basis, determination of the decomposition levels, selection of the threshold, and threshold function [12]. Most of them require manual setting, which dramatically limits the wide application of the wavelet transform [13]. To address this, we propose an improved **W**avelet **A**dapti**V**e **d**E**noising (iWAVE) method to enhance both applicability and performance, improving the robustness of the classification method. Feature extraction is vital for bird vocalization classification, and its performance is highly dependent on the quality of the feature. Although the Mel-frequency cepstral coefficients (MFCCs) have been widely used, we prefer the Mel-filter bank (FBank) due to its lower computational requirements and higher feature correlations. FBank tends to emphasize low-frequency over high-frequency information during feature extraction. As a solution, ian nverse FBank (iFBank) was introduced and combined with FBank to form a bidirectional FBank (BiFBank). This novel approach integrates low-frequency and high-frequency information and achieves the best classification performance on the tested features by using the random forest (RF) classifier [14]. Therefore, we propose the iWAVE and BiFBank methods for adaptive automatic recognition, aiming to improve classification performance and robustness in bird vocalization. The main contributions of this study are as follows:  
1. We constructed the iWAVE wavelet threshold denoising method for bird vocalization, which adapts to determine the optimal decomposition levels, optimizes the threshold selection rule, and improves the threshold function. This method outperforms existing classical and improved techniques in most cases, thus supporting the wider application of wavelet-transform-based denoising. 2. We developed a horizontal linear fusion of FBank and iFBank to create BiFBank features, effectively integrating low-frequency and high-frequency information, reducing the loss of audio data, and making up for the shortcomings of traditional methods. 3. We demonstrated that FBank outperforms MFCC under the same conditions, providing researchers with a wider selection of features. The proposed iWAVE-BiFBank method achieves optimal performance and robustness on the tested features and compared with state-of-the-art methods.