# Open-Set Recognition of Pansori Rhythm Patterns Based on Audio Segmentation
## 1 Introduction
This configuration enables the identification of unknown classes in post-processing, as unknown samples maintain greater distances from all known class clusters in the embedding space. Semantic segmentation presents greater complexities than classification within the framework of open-set recognition Ref. [11]. In the context of open-set classification, each object within a single image is uniformly classified into one category. Conversely, segmentation involves partitioning an image into multiple segments and assigning the same label to pixels corresponding to the same object, thus highlighting the significant contextual relationships among adjacent pixels. [PERSON]. [12] introduced the OpenFCN framework, which is composed of two processing stages. Initially, an encoder network extracts discriminative features. These features, along with their associated probability distributions, are then processed through Weibull libMR models Ref. [13] to determine the probability logits for unknown classes. Building upon previous studies, [PERSON]. [14; 15] utilized the negative of the maximum unnormalized logit to identify outlier samples. Additionally, [PERSON]. [16] implemented a post-meta-classifier aimed at reducing false positives by utilizing pixel-wise uncertainty measures derived from the logit probability distribution. It should be noted, however, that low-confidence predictions are not exclusively associated with unknown samples. A fundamental principle of open-set recognition is the establishment of a compact cluster structure in the feature space for known classes that aids in the identification of unknown classes by providing ample space to accommodate these unknown samples within the embedded feature space. In accordance with this principle, [PERSON]. [17] developed a well-clustered embedding space, optimizing the model in relation to discriminative cross-entropy loss. In this study, we address the challenge of identifying unknown Passori rhythm patterns within the context of open-set recognition by developing a lightweight audio segmentation framework. Unlike image segmentation, which operates within a two-dimensional space, audio segmentation decisions are made along the temporal axis. This approach allows our lightweight framework to simplify the decision-making process by focusing exclusively on the temporal axis and reducing the reliance on the width axis (Mel bins). The segmentation features of a sample are refined by minimizing both center and supervised contrastive losses, whereby pixels from different classes are driven apart, while pixels from the same class are drawn together. In our empirical analysis, we initially assess the performance of our segmentation framework on dataset of Passori rhythm patterns, demonstrating that the proposed loss significantly enhances the ability to distinguish unknown rhythm patterns. Subsequent comparative experiments reveal that our framework outperforms traditional semantic segmentation networks in audio segmentation tasks. Lastly, we validate the robustness of our proposed method through its application to various acoustic datasets. The contributions of the paper can be summarized as follows:  
1. We introduce an innovative framework for segmentation aimed at identifying unrecognized Passori rhythm patterns or sound events from established categories within audio sequences. To our knowledge, scant research has been conducted on open-set segmentation within the domains of music and sound. 2. We adapted both center loss and supervised contrastive loss, originally utilized in classification tasks, to the segmentation challenge in order to enhance intra-class compactness and promote inter-class dispersion. 3. Our framework demonstrates the efficient detection of Passori rhythm patterns and offers scalability across diverse acoustic datasets. The experiments show that our proposed method has the potential to increase the open-set performance in various acoustic fields.