## 1 Introduction  
Animal sound classification (ASC) has emerged as a crucial tool in wildlife monitoring systems, as it can identify different animal species based on their unique sounds [1]. ASC is particularly useful when visual identification is challenging, such as regarding small, nocturnal, and camouflaged animals [2]. Recently, deep learning-based models, such as convolutional neural networks (CNNs), have demonstrated superior performance in ASC as well as in other signal processing applications [3, 4]. However, supervised deep learning ASC models (DeepASC) require huge amounts of accurately labeled data, and acquiring such data is an expensive and time-intensive process [5]. Insufficient animal sound data for DeepASC may result in poor classification performance due to overfitting or generalization failure [6, 7, 8]. This lack of labeled data is particularly severe in ASCs due to (1) the diversity of species and sounds, (2) limited access to certain habitats, especially remote or protected areas where specific species reside, (3) the time-consuming labeling process of animal sounds, and (4) the availability of experts, especially for animals that are difficult to observe (e.g., rare or endangered species). In these cases, DeepASC may not work effectively. To mitigate this problem, few-shot learning that involves learning new tasks from only a small number of samples has garnered considerable attention. Recently, meta-learning, or learning to learn, has been widely used as one of the noteworthy methodologies for few-shot learning. Meta-learning allows deep learning-based classification models to learn common prior knowledge shared across different tasks. Starting from the common knowledge, the classification models can easily learn specific knowledge about unseen tasks even with limited data samples. Model-agnostic meta-learning (MAML), one of the most successful meta-learning methods, embeds the common knowledge derived from various tasks into the parameter initialization of the model [9]. Pretrained parameter initialization serves as a good starting point to overcome the data shortage problem and achieve good performance. Due to these properties, many recent works such as few-shot image classification [9], anomaly detection [10], and influenza forecasting [11] have aimed to achieve better pretrained generalization with MAML. MAML is particularly useful in ASC because it enables DeepASC to learn common knowledge across various ASC source tasks, effectively adapting to specific knowledge of the target ASC task. Here, the target task is a classification task for rare or obscure animal species for which obtaining large amounts of labeled data is difficult. However, if the knowledge of the source tasks is too diverse, MAML often fails to generalize their common knowledge. As a result, the prior knowledge encoded in this failed generalization may be useful in some tasks but not in other tasks. This can be overcome by accommodating both task-wide general and task-specific knowledge into MAML-initialized parameters. To carry out that, in this study, we propose a novel task-adaptive parameter transformation (TAPT) scheme that directly transforms the initial parameters of DeepASC according to the suitability of each task during the meta-learning. For parameter transformation, we consider gradients of initial parameters of DeepASC as the suitability to the task and use them as input to a regression model to learn task-specific knowledge. Then, we generate transformation variables from the regression model as output while generalizing common knowledge across tasks. Finally, these variables are used to adapt the initial parameters of DeepASC to the target task. Unlike traditional MAML, where initialization remains static across tasks, TAPT dynamically transforms the initial parameters based on task-specific knowledge. This property makes TAPT more flexible than MAML and more effective in classifying sounds of diverse species.