# Multi-Scale Deep Feature Fusion with Machine Learning Classifier for Birdsong Classification
###### Abstract
Among other things, for birdsong recognition tasks, this led to further improvements in network performance and generalization capabilities. Subsequent research revealed that there are temporal sequence relationships within birdsong, whereby the acoustic patterns evolve over time. This discovery prompted researchers to introduce sequence modeling methods into birdsong recognition, as demonstrated by the work of [23; 24]. By considering temporal dependencies, these methods better capture the dynamic characteristics of birdsong, thereby improving recognition performance. Furthermore, some researchers believe that feature fusion based on multi-scale or multi-view methods can effectively improve recognition accuracy in convolutional neural  
Figure 1: Birdsong recognition task. networks. Common multi-scale feature fusion methods include using convolutional kernels of different sizes to extract local and global features, as shown in the work of [8], etc. On the other hand, multi-view feature fusion methods, as demonstrated in the studies of [25; 26; 27; 28], can also improve recognition performance. This paper mainly focuses on another approach based on multi-scale feature fusion, which captures features at different levels through pyramid structures. The design of this method is primarily inspired by the feature extraction backbone network (Darknet) in the object detection model YOLO [29; 30], aiming to achieve efficient capture and fusion of multi-scale features. As shown in Figure 2, the feature pyramid approach can simultaneously extract features at multiple scales, which, compared to single-scale feature extraction methods, is more effective at capturing both global and local feature information. Compared to traditional multi-scale convolution schemes, the feature pyramid method processes features at different levels without modifying the main structure of the model, allowing for more flexible fusion of multi-scale information, thereby improving the model's performance. To address the above issues, this paper proposes a multi-scale feature extraction and classification method based on deep learning techniques, aiming to effectively capture the features of birdsong signals. The study is based on a manually labeled public bird dataset ([[https://data.baai.ac.cn/details/Birdsdata](https://data.baai.ac.cn/details/Birdsdata)]([https://data.baai.ac.cn/details/Birdsdata](https://data.baai.ac.cn/details/Birdsdata)), accessed on 9 February 2025), which consists of 2 s audio clips with significant noise interference, as well as birdsong signals previously processed by our research team. The main contributions of this study are as follows:  
* Based on the feature pyramid scheme, global average pooling (GAP) layers are introduced at different levels of ResNet18 to extract multi-scale features, enhancing the ability to capture both local and global features in birdsong signals. * The work improves classification accuracy by introducing multiple machine learning classifiers that adapt to different data distributions in bird species' vocalizations, ensuring better performance in different ecological monitoring environments. * The method is validated based on three distinct spectral representations, highlighting its generalizability and robustness. The remainder of this paper is organized as follows: The Section 2 presents the proposed method. The Section 3 provides the experimental setup, and introduction to the dataset and the parameters of the spectral features. The Section 4 presents a discussion of the experimental results. Finally, the Section 5 summarizes the findings and suggests future directions for research. Figure 2: Schematic diagram of multi-scale feature extraction and single-scale feature extraction. (**a**) Schematic diagram of multi-scale feature extraction. (**b**) Single-scale feature extraction diagram.