Unsupervised Environmental Sound Classification Based On Topological Persistence

Cao, Yueqi; Zhang, Shiqiang; Yan, Fang-Jia; Li, Wenyu; Sun, Fupeng; Sun, Hongwei

doi:10.1109/icsidp47821.2019.9173135

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Cited by 3 publications

(1 citation statement)

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“…These trends can reflect differences in the structure in the data. Typically, we use a given filtration to calculate and record changes to the topological features [15,16] .…”

Section: Persistent Homologymentioning

confidence: 99%

An efficient 3D point cloud classification approach via persistent homology

Zhou,

Pan,

Zhang

et al. 2024

Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024)

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“…These trends can reflect differences in the structure in the data. Typically, we use a given filtration to calculate and record changes to the topological features [15,16] .…”

Section: Persistent Homologymentioning

confidence: 99%

An efficient 3D point cloud classification approach via persistent homology

Zhou,

Pan,

Zhang

et al. 2024

Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024)

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An Expert System for Identification of Domestic Emergency based on Normal and Abnormal Sound

Shilaskar¹,

Bhatlawande²,

Vaishale³

et al. 2023

2023 Somaiya International Conference on Technology and Information Management (SICTIM)

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University Academic Performance Development Prediction Based on TDA

Zhou

Pan

et al. 2022

Entropy

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With the rapid development of higher education, the evaluation of the academic growth potential of universities has received extensive attention from scholars and educational administrators. Although the number of papers on university academic evaluation is increasing, few scholars have conducted research on the changing trend of university academic performance. Because traditional statistical methods and deep learning techniques have proven to be incapable of handling short time series data well, this paper proposes to adopt topological data analysis (TDA) to extract specified features from short time series data and then construct the model for the prediction of trend of university academic performance. The performance of the proposed method is evaluated by experiments on a real-world university academic performance dataset. By comparing the prediction results given by the Markov chain as well as SVM on the original data and TDA statistics, respectively, we demonstrate that the data generated by TDA methods can help construct very discriminative models and have a great advantage over the traditional models. In addition, this paper gives the prediction results as a reference, which provides a new perspective for the development evaluation of the academic performance of colleges and universities.

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Unsupervised Environmental Sound Classification Based On Topological Persistence

Cited by 3 publications

References 18 publications

An efficient 3D point cloud classification approach via persistent homology

An efficient 3D point cloud classification approach via persistent homology

An Expert System for Identification of Domestic Emergency based on Normal and Abnormal Sound

University Academic Performance Development Prediction Based on TDA

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