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Clustering Acoustic Segments Using Multi-Stage Agglomerative Hierarchical Clustering

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  • Lerato Lerato
  • Thomas Niesler

Abstract

Agglomerative hierarchical clustering becomes infeasible when applied to large datasets due to its O(N2) storage requirements. We present a multi-stage agglomerative hierarchical clustering (MAHC) approach aimed at large datasets of speech segments. The algorithm is based on an iterative divide-and-conquer strategy. The data is first split into independent subsets, each of which is clustered separately. Thus reduces the storage required for sequential implementations, and allows concurrent computation on parallel computing hardware. The resultant clusters are merged and subsequently re-divided into subsets, which are passed to the following iteration. We show that MAHC can match and even surpass the performance of the exact implementation when applied to datasets of speech segments.

Suggested Citation

  • Lerato Lerato & Thomas Niesler, 2015. "Clustering Acoustic Segments Using Multi-Stage Agglomerative Hierarchical Clustering," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-24, October.
    • Handle: RePEc:plo:pone00:0141756
    DOI: 10.1371/journal.pone.0141756
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    References listed on IDEAS

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    1. Jiandong Yin & Jiawen Yang & Qiyong Guo, 2014. "Evaluating the Feasibility of an Agglomerative Hierarchy Clustering Algorithm for the Automatic Detection of the Arterial Input Function Using DSC-MRI," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-9, June.
    2. William Day & Herbert Edelsbrunner, 1984. "Efficient algorithms for agglomerative hierarchical clustering methods," Journal of Classification, Springer;The Classification Society, vol. 1(1), pages 7-24, December.
    3. Fionn Murtagh & Pierre Legendre, 2014. "Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion?," Journal of Classification, Springer;The Classification Society, vol. 31(3), pages 274-295, October.
    4. Roy Varshavsky & David Horn & Michal Linial, 2008. "Global Considerations in Hierarchical Clustering Reveal Meaningful Patterns in Data," PLOS ONE, Public Library of Science, vol. 3(5), pages 1-10, May.
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    1. Tianxiao Wang & Zhecheng Jing & Shupei Zhang & Chengqun Qiu, 2023. "Utilizing Principal Component Analysis and Hierarchical Clustering to Develop Driving Cycles: A Case Study in Zhenjiang," Sustainability, MDPI, vol. 15(6), pages 1-13, March.

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