Open-Set Recognition of Pansori Rhythm Patterns Based on Audio Segmentation

Author:

You Jie12ORCID,Lee Joonwhoan2

Affiliation:

1. School of Information Engineering, East China Jiaotong University, Nanchang 330013, China

2. Artificial Intelligence Laboratory, Department of Computer Science and Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea

Abstract

Pansori, a traditional Korean form of musical storytelling, is characterized by performances involving a vocalist and a drummer. It is well-known for the singer’s expressive narrative (aniri) and delicate gesture with fan in hand. The classical Pansori repertoires mostly tell love, satire, and humor, as well as some social lessons. These performances, which can extend from three to five hours, necessitate that the vocalist adheres to precise rhythmic structures. The distinctive rhythms of Pansori are crucial for conveying both the narrative and musical expression effectively. This paper explores the challenge of open-set recognition, aiming to efficiently identify unknown Pansori rhythm patterns while applying the methodology to diverse acoustic datasets, such as sound events and genres. We propose a lightweight deep learning-based encoder–decoder segmentation model, which employs a 2-D log-Mel spectrogram as input for the encoder and produces a frame-based 1-D decision along the temporal axis. This segmentation approach, processing 2-D inputs to classify frame-wise rhythm patterns, proves effective in detecting unknown patterns within time-varying sound streams encountered in daily life. Throughout the training phase, both center and supervised contrastive losses, along with cross-entropy loss, are minimized. This strategy aimed to create a compact cluster structure within the feature space for known classes, thereby facilitating the recognition of unknown rhythm patterns by allocating ample space for their placement within the embedded feature space. Comprehensive experiments utilizing various datasets—including Pansori rhythm patterns (91.8%), synthetic datasets of instrument sounds (95.1%), music genres (76.9%), and sound datasets from DCASE challenges (73.0%)—demonstrate the efficacy of our proposed method to detect unknown events, as evidenced by the AUROC metrics.

Funder

National Research Foundation of Korea

Publisher

MDPI AG

Reference47 articles.

1. Pandeya, Y.R., Bhattarai, B., and Lee, J. (2022). Tracking the rhythm: Pansori rhythm segmentation and classification methods and datasets. Appl. Sci., 12.

2. Recent advances in open set recognition: A survey;Geng;IEEE Trans. Pattern Anal. Mach. Intell.,2020

3. Saito, K., Kim, D., and Saenko, K. (2021). OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers. arXiv.

4. Heras, J., Royo, D., and Ángel, M. (2024, June 28). A Good Closed-Set Classifier Is All You Need for the AIROGS Challenge. Available online: http://rumc-gcorg-p-public.s3.amazonaws.com/evaluation-supplementary/644/d79d3e55-505a-416a-b389-0a51170b1271/AIROGS.pdf.

5. Distance metric learning for large margin nearest neighbor classification;Weinberger;J. Mach. Learn. Res.,2009

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