Clustering algorithm for audio signals based on the sequential Psim matrix and Tabu Search

Clustering algorithm for audio signals based on the sequential Psim matrix and Tabu Search

Abstract Audio signals are a type of high-dimensional data, and their clustering is critical. However, distance calculation failures, inefficient index trees, and cluster overlaps, derived from the equidistance, redundant attribute, and sparsity, respectively, seriously affect the clustering perform...

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Bibliographic Details
Main Authors: Wenfa Li, Gongming Wang, Ke Li
Format: Article
Language:English
Published: SpringerOpen 2017-12-01
Series:EURASIP Journal on Audio, Speech, and Music Processing
Subjects:
Audio signal clustering
Sequential Psim matrix
Tabu Search
Heuristic search
K-Medoids
Spectral clustering
Online Access:http://link.springer.com/article/10.1186/s13636-017-0123-3

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http://link.springer.com/article/10.1186/s13636-017-0123-3

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