Clustering acoustic segments using multi- stage agglomerative hierarchical clustering
Date
2015
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Public Library of Science
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.
AFRIKAANSE OPSOMMING: Geen opsomming beskikbaar
AFRIKAANSE OPSOMMING: Geen opsomming beskikbaar
Description
CITATION: Lerato, L. & Niesler, T. 2015. Clustering acoustic segments using multi- stage agglomerative hierarchical clustering. PLoS ONE 10(10):1-24, doi:10.1371/journal.pone.0141756.
The original publication is available at http://journals.plos.org/plosone
The original publication is available at http://journals.plos.org/plosone
Keywords
Agglomerations, Industrial, Document clustering, Acoustical engineering
Citation
Lerato, L. & Niesler, T. 2015. Clustering acoustic segments using multi- stage agglomerative hierarchical clustering. PLoS ONE 10(10):1-24, doi:10.1371/journal.pone.0141756