Semi-supervised feature selection for audio classification based on constraint compensated Laplacian score

Xu Kui Yang, Liang He, Dan Qu, Wei Qiang Zhang, Michael T. Johnson

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Audio classification, classifying audio segments into broad categories such as speech, non-speech, and silence, is an important front-end problem in speech signal processing. Dozens of features have been proposed for audio classification. Unfortunately, these features are not directly complementary and combining them does not improve classification performance. Feature selection provides an effective mechanism for choosing the most relevant and least redundant features for classification. In this paper, we present a semi-supervised feature selection algorithm named Constraint Compensated Laplacian score (CCLS), which takes advantage of the local geometrical structure of unlabeled data as well as constraint information from labeled data. We apply this method to the audio classification task and compare it with other known feature selection methods. Experimental results demonstrate that CCLS gives substantial improvement.

Original languageEnglish
Article number9
JournalEurasip Journal on Audio, Speech, and Music Processing
Issue number1
StatePublished - Dec 1 2016

Bibliographical note

Publisher Copyright:
© 2016, Yang et al.


  • Audio classification
  • Constraint information
  • Locality preserving
  • Semi-supervised feature selection

ASJC Scopus subject areas

  • Acoustics and Ultrasonics
  • Electrical and Electronic Engineering


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