Recognition of sounds using square cauchy mixture distribution

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this paper, a new probability density distribution, 'the square Cauchy mixture distribution' is proposed for recognition of sound. The proposed density is based on the Cauchy distribution and modified so that it has mean and variance. Since the proposed density can be calculated using only simple arithmetic operations, it can be calculated faster than the Gaussian mixture model (GMM). In addition to the definition of the proposed distribution, a parameter estimation method based on the gradient descent is also described. Two experiments were conducted such as recognition of environmental sound and recognition of singer of the singing voice. The results of the experiments revealed that the proposed method was 10% to 15% faster than the GMM with addlog operation and the recognition performance was comparable.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages726-730
Number of pages5
ISBN (Electronic)9781509023769
DOIs
Publication statusPublished - 2017 Mar 27
Event2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016 - Beijing, China
Duration: 2016 Aug 132016 Aug 15

Publication series

Name2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016

Other

Other2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016
CountryChina
CityBeijing
Period16/8/1316/8/15

Keywords

  • Gaussian distribution
  • addlog
  • cauchy distribution
  • environmental sound recognition
  • singer recognition
  • squared cauchy distribution

ASJC Scopus subject areas

  • Signal Processing

Fingerprint Dive into the research topics of 'Recognition of sounds using square cauchy mixture distribution'. Together they form a unique fingerprint.

Cite this