Speech based gender identification using empirical mode decomposition (EMD)
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Date
2014-04Publisher
BRAC UniversityAuthor
Chowdhury, Mahpara HyderMetadata
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Traditionally for feature extraction, decomposition techniques such as Fourier
decomposition are used to capture signals. But those methods have some margins
like – it only works for linear and stationary data. On the other hand, in real world,
we found data that are in non-linear and non-stationary. EMD or Empirical Mode
Decomposition technique is a new approach introduced by Huang et al (1998) that
can take any complicated signal and decomposed it to IMF. It extracts the
amplitude and frequency information of a signal at a particular time. It is robust for
non-linear and non-stationary signal processing. In this paper, I am using EMD as
a new approach for gender identification based on speech signal. Gender
identification based on the voice of a speaker consists of detecting if a speech
signal is given by a male or female. Detecting the gender of a speaker has several
applications.