# A Multi-Scale Feature Fusion Hybrid Convolution Attention Model for Birdsong Recognition
## 1 Introduction  
Birds play an important role in ecosystems as key biological indicators and are closely linked to humans [1]. By analyzing bird calls, researchers can gain insights into bird behavior, population dynamics and ecosystem health, which are crucial for the conservation and management of ecosystems [2]. Image recognition technology has made significant progress in bird research, such as [PERSON] and [PERSON] [3], who used deep convolutional neural networks (DCNNs) to classify bird images based on high-dimensional representations. However, collecting such data in a complex forest environment is still quite challenging. In contrast, bird vocalization-based recognition methods provide solutions to these challenges with their penetability, efficiency, non-invasiveness and wide applicability [4]. This approach has become indispensable in ecological research and biodiversity monitoring, significantly increasing the efficiency and coverage of data collection. Developing methods for birdsong recognition usually involves four steps [3]: sample collection, signal preprocessing, feature extraction and fusion, model recognition result output. Among these, feature extraction plays a pivotal role in determining the model's performance. Recent studies emphasize the need for selecting appropriate feature representations tailored to birdsong characteristics in order to achieve reliable classification results [5]. A common approach involves converting raw audio into image-like spectrogram representations, which are then fed into neural networks to capture temporal and spectral textures. Widely used spectral features include log-Mel spectrograms, Mel-Frequency Cepstral Coefficients (MFCCs), Gammatone Cepstral Coefficients (GTCCs), wavelet transforms (WTs), Hilbert-Huang Transforms (HHTs), Short-Time Fourier Transforms (STFTs) and Linear Predictive Cepstral Coefficients (LPCCs) [6]. Early research mainly relied on traditional feature extraction and classification methods. Manual feature extraction, such as Mel-Frequency Cepstral Coefficient (MFCC) and Linear Predictive Coding (LPC), is then combined with classical machine learning classifiers, such as Support Vector Machine (SVM) and Hidden Markov Model (HMM), for birdsong recognition and detection [7]. However, these approaches often fail to capture complex patterns and contextual information, limiting model performance and generalization. With the advent of deep learning, automated high-level feature extraction has significantly enhanced recognition performance while reducing the need for manual preprocessing. For instance, [PERSON] et al. applied an Inception-v3 architecture to the BirdCLEF2019 dataset to classify 695 bird species, achieving a modest accuracy of 16% [5]. They pointed out that due to the shift-invariance and parameter-sharing characteristics of CNN, it was difficult for CNN to distinguish the spectrograms that occur at different frequencies but have similar shapes. To address these limitations, recent studies have introduced multi-scale and attention-based architectures. [PERSON] et al. proposed the Ensemble Multi-Scale CNN (EMSCNN), which achieved 91.49% accuracy on 30 bird species by concatenating features from convolutional kernels of varying sizes [8]. However, the method had many model parameters, which made training and inference time-consuming. In addition, the multi-scale features were directly cascaded, and the possible correlations and dependencies between these features were not deeply explored and utilized. [PERSON] and [PERSON] [9] discussed the classification of 10 bird species on ResNet50, CNN and VGG-16 networks, with classification accuracies of 96.3%, 93.7% and 91.9%, respectively. Experiments show that these methods were significantly better than the MFCC-DNN method based on sound signals. [PERSON] et al. proposed MFF-ScEnet, which fuses Mel spectrogram features with Sinc features to address the poor performance of single features for classification [10]. It reached 96.66% on 20 bird species and 96.28% on 15 self-built datasets.