# Multi-Scale Deep Feature Fusion with Machine Learning Classifier for Birdsong Classification
###### Abstract  
Birds are significant bioindicators in the assessment of habitat biodiversity, ecological impacts and ecosystem health. Against the backdrop of easier bird vocalization data acquisition, and with deep learning and machine learning technologies as the technical support, exploring recognition and classification networks suitable for bird calls has become the focus of bioacoustics research. Due to the fact that the spectral differences among various bird calls are much greater than the differences between human languages, constructing birdsong classification networks based on human speech recognition networks does not yield satisfactory results. Effectively capturing the differences in birdsong across species is a crucial factor in improving recognition accuracy. To address the differences in features, this study proposes multi-scale deep features. At the same time, we separate the classification part from the deep network by using machine learning to adapt to classification with distinct feature differences in birdsong. We validate the effectiveness of multi-scale deep features on a publicly available dataset of 20 bird species. The experimental results show that the accuracy of the multi-scale deep features on a log-wavelet spectrum, log-Mel spectrum and log-power spectrum reaches 94.04%, 97.81% and 95.89%, respectively, achieving an improvement over single-scale deep features on these three spectrograms. Comparative experimental results show that the proposed multi-scale deep feature method is superior to five state-of-the-art birdsong identification methods, which provides new perspectives and tools for birdsong identification research, and is of great significance for ecological monitoring, biodiversity conservation and forest research. b ARTIC 2025 15 1885 overcome the limitations of traditional approaches with their penetrating, efficient, non-invasive and versatile nature [5]. These methods have become important tools for ecological research and biodiversity monitoring. By improving data collection efficiency and coverage, they have enabled researchers to gain a deeper understanding of bird behavior patterns, population dynamics and ecosystem health [6]. Such analyses are essential for assessing and protecting ecosystems. Birdsong identification consists of four phases: sample collection, signal pre-processing, feature extraction and fusion, and model classification that outputs identification results, as described in [7]. The workflow of the task is shown in Figure 1.  
Feature extraction plays a crucial role in the performance of birdsong recognition. Exploring suitable feature extraction and fusion methods has become one of the focal points of birdsong classification. The study by [8] showed that different neural network models extracted different features with different classification accuracies, which highlights the importance of feature extraction in birdsong classification. Commonly used birdsong recognition methods are mainly image-based, involving the conversion of audio signals into time-frequency representations. Spectra or features commonly used for birdsong classification mainly include the log-Mel spectrum, wavelet transform (WT) spectrum [9], Hilbert-Huang transform (HHT) spectrum and short-time Fourier transform (STFT) spectrum, as well as features such as Mel-frequency cepstral coefficients (MFCCs) [10], gamma-tone cepstral coefficients (GTCCs) and linear predictive cepstral coefficients (LPCCs) [11]. These spectra are fed into convolutional neural networks (CNNs) to extract key textures and structures, followed by classification through fully connected layers. A major representative of this approach is the convolutional neural network (CNN), which has been utilized by [12; 13; 14; 15; 16], etc. However, as the number of CNN layers increases, it has been found that CNNs are prone to overfitting. Accordingly, [PERSON] et al. [17] proposed a residual structure convolutional neural network (ResNet), which was later adopted by [18; 19; 20; 21; 22], etc.