## 2 Related Works  
In this section, we first present a brief literature review on deep learning-based ASC, and then introduce several few-shot learning schemes to overcome the data shortage situation in ASC.  
First of all, Sasmaz et al. proposed a deep learning-based framework for classifying the sounds of various animal species, such as birds, cats, and dogs [12]. They collected 875 sound samples from 10 different animal species from an online sound source site and preprocessed animal audio data into mel-spectrograms. They then constructed three convolution layers with a max pooling layer and trained this model to classify target animal species.  
Xie et al. proposed a bird sound classification structure that can incorporate acoustic features, visual features, and generalized features from a deep learning model; the first two features were obtained using traditional classifiers K-nearest neighbor and random forest, respectively, and the last generalized features were extracted from a three-layer CNN model [3]. Finally, the bird species is identified by incorporating these three features using a late fusion technique.  
On the other hand, Zhang et al. proposed a method that can achieve outstanding bird sound classification accuracy based on deep CNNs (DCNNs) [4,13]. Specifically, they calculated spectrograms of the short-time Fourier transform, Mel-frequency transform, and Chirplet transform for animal sounds, constructed individual DCNN models for each spectrogram, and predicted bird species by combining the features from the DCNN models. Furthermore, they used a transfer-learning (TL) scheme to reduce the number of trainable parameters of the fusion model.  
Liao et al. proposed a domestic pig sound classification model called TransformerCNN [14]. The model consists of two network modules, CNN and Transformer, in a parallel structure [15]. They decided that the spatial features extracted using a 4 conv CNN (CNN with four convolutional layers) were not sufficient for ASC, and added sequential coding of the Transformer module to capture global features from the input spectrogram. The parallel structure model of the two modules effectively extracted richer information from different signals than a single structure model, and showed excellent performance in pig sound recognition.  
The aforementioned deep learning-based ASC models usually require a large amount of labeled training data to achieve good performance. In addition, these models can only classify the animal species they are trained on. However, in the case of animals that are difficult to observe, the few-shot learning scheme, which learns how to classify animal species with a few data samples, has attracted great attention in situations of data shortage. For instance, Shi et al. proposed a few-shot acoustic event detection scheme based on three supervised learning schemes and three meta-learning schemes [16]: MetaOptNet [9], MAML [17], and Prototypical Networks (ProtoNet). For the Audioset [18] dataset containing music and animal sounds, they compared those learning schemes in 5-way 1-shot and 5-way 5-shot settings. As a result, all meta-learning schemes outperformed the supervised learning schemes, and ProtoNet was the best among the meta-learning schemes.  
Meanwhile, many high-ranked methods for few-shot bioacoustics event detection presented in the DCASE 2022 challenge [19] used ProtoNet as a learning scheme for training CNN-based ASC models. Here, bioacoustics event detection refers to locating animal sounds in audio recordings and classifying animal species. Although bioacoustics event detection is slightly different from ASC, this indicates that ProtoNet is considered an effective model for ASC.