TY - GEN
T1 - Spectral Analysis of Familiar Human Voice Based On Hilbert-Huang Transform
AU - Gumelar, Agustinus Bimo
AU - Purnomo, Mauridhi Hery
AU - Yuniarno, Eko Mulyanto
AU - Sugiarto, Indar
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Spectral analysis of human voice signals is important to reveal hidden information when is not available in the time-domain. Extracting spectral information from those voice signals will enhance our knowledge in understanding the nature and characteristic of the voice. It concerned with the decomposition method of voice signals into simpler components in frequency and time. The frequency analysis tools are also give beneficial for describing the spectral distribution in a voice signal, very often the methods used by the tools have limitations that restrict us to interpret the data properly. This paper describes a powerful data analysis method called the Hilbert-Huang transform (HHT), which can be used to extract audio frequency components from nonlinear and nonstationary human voice signals. It can describe the audio frequency components locally and adaptively for nearly any oscillating signal. This makes it very extremely versatile to be used for analysing familiar human voices.
AB - Spectral analysis of human voice signals is important to reveal hidden information when is not available in the time-domain. Extracting spectral information from those voice signals will enhance our knowledge in understanding the nature and characteristic of the voice. It concerned with the decomposition method of voice signals into simpler components in frequency and time. The frequency analysis tools are also give beneficial for describing the spectral distribution in a voice signal, very often the methods used by the tools have limitations that restrict us to interpret the data properly. This paper describes a powerful data analysis method called the Hilbert-Huang transform (HHT), which can be used to extract audio frequency components from nonlinear and nonstationary human voice signals. It can describe the audio frequency components locally and adaptively for nearly any oscillating signal. This makes it very extremely versatile to be used for analysing familiar human voices.
KW - Hilbert Huang Transform
KW - Hilbert Spectrum
KW - Human Voice Analysis
KW - Spectral Analysis
UR - http://www.scopus.com/inward/record.url?scp=85066506227&partnerID=8YFLogxK
U2 - 10.1109/CENIM.2018.8710943
DO - 10.1109/CENIM.2018.8710943
M3 - Conference contribution
AN - SCOPUS:85066506227
T3 - 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Proceeding
SP - 311
EP - 316
BT - 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Proceeding
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018
Y2 - 26 November 2018 through 27 November 2018
ER -