A New Time-Frequency Speech Analysis Approach Based On Adaptive Fourier Decomposition
In this paper, a new adaptive Fourier decomposition
(AFD) based time-frequency speech analysis approach is proposed.
Given the fact that the fundamental frequency of speech signals often
undergo fluctuation, the classical short-time Fourier transform (STFT)
based spectrogram analysis suffers from the difficulty of window size
selection. AFD is a newly developed signal decomposition theory. It is
designed to deal with time-varying non-stationary signals. Its
outstanding characteristic is to provide instantaneous frequency for
each decomposed component, so the time-frequency analysis becomes
easier. Experiments are conducted based on the sample sentence in
TIMIT Acoustic-Phonetic Continuous Speech Corpus. The results
show that the AFD based time-frequency distribution outperforms the
STFT based one.
<p>[1] L. Cohen, Time-Frequency Analysis: Theory and Applications, Prentice,
Hall, 1995.
[2] S. Qian and D. Chen, “Joint Time-Frequency Analysis”, IEEE Signal
Processing Magazine, pp. 53-67, March, 1999.
[3] H. Choi and W.J. Williams, “Improved Time-Frequency Representation
of Multicomponent Signals Using Exponential Kernel,” IEEE
Transactions on Acoustics, Speech, and Signal Processing, Vol.37, No.6,
pp. 862-871, June 1989.
[4] L. Cohen, “Time-Frequency Distributions -- A Review”, Proceedings of
the IEEE, vol.77, No.7, pp.941-981, 1989.
[5] T.A.C.M. Claasen and W.F.G. Mecklenbrauker, “The Wigner
distribution-a tool for time-frequency signal analysis, Part III--Relations
with other time-frequency signal transformations”, Philips Journal of
Research, Vol. 35, No. 6. pp.372-389, 1980.
[6] B. Boashash, Time-Frequency Signal Analysis and Processing – A
Comprehensive Reference, Elsevier Science, Oxford, 2003.
[7] M.Kepesi and L. Weruaga, “Adaptive chirp-based time–frequency
analysis of speech signals”, Speech Communication, 48, pp. 474-492,
2006.
[8] T. Qian, “Intrinsic Mono-components Decomposition of Functions: An
Advance of Fourier Theory”, Math.Meth.Appl.Sci. 33, pp.880-891, 2010.
[9] T. Qian and Y. Wang, “Adaptive Fourier Series – a Variation of Greedy
Algorithm”, Advances in Computational Mathematics, 34, no.3,
pp.279-293, 2011.
[10] D. Gabor, “Theory of Communication”, Journal of the IEE, vol.93,
pp.~429-457, 1946.
[11] T. Qian, Q. H. Chen and L.Q. Li, “Analytic unit quadrature signals with
non-linear phase”, Physica D: Nonlinear Phenomena, 303, 80-87 2005.
[12] T. Qian, “Characterization of boundary values of functions in Hardy
spaces with applications in signal analysis”, Journal of Integral Equations
and Applications, Volume 17, Number 2, pp 159-198, 2005.
[13] T. Qian, “Analytic Signals and Harmonic Measures”, J. Math. Anal.
Appl. 314, pp.526-536, 2006.
[14] T. Qian, L. Zhang and Z. Li, “Algorithm of Adaptive Fourier Transform”,
IEEE Transactions on Signal Processing, vol.59(12),5899-5906,
Dec.,2011.
[15] T. Qian, “Mono-components for decomposition of signals”,
Mathematical Methods in the Applied Sciences, 29, pp. 1187-1198,
2006..
[16] T. Qian, “Boundary Derivatives of the Phases of Inner and Outer
Functions and Applications”, Mathematical Methods in the Applied
Sciences, 32, pp. 253-263, 2009.
[17] T. Qian and E. Wegert, “Optimal Approximation by Blaschke Forms”,
Complex Variables and Elliptic Equations, preprint Available online: 20
Jun 2011.
[18] TIMIT Acoustic-Phonetic Continuous Speech Corpus,
http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC93S
1 (Retrieved on 27/11/2012)</p>
<p>[1] L. Cohen, Time-Frequency Analysis: Theory and Applications, Prentice,
Hall, 1995.
[2] S. Qian and D. Chen, “Joint Time-Frequency Analysis”, IEEE Signal
Processing Magazine, pp. 53-67, March, 1999.
[3] H. Choi and W.J. Williams, “Improved Time-Frequency Representation
of Multicomponent Signals Using Exponential Kernel,” IEEE
Transactions on Acoustics, Speech, and Signal Processing, Vol.37, No.6,
pp. 862-871, June 1989.
[4] L. Cohen, “Time-Frequency Distributions -- A Review”, Proceedings of
the IEEE, vol.77, No.7, pp.941-981, 1989.
[5] T.A.C.M. Claasen and W.F.G. Mecklenbrauker, “The Wigner
distribution-a tool for time-frequency signal analysis, Part III--Relations
with other time-frequency signal transformations”, Philips Journal of
Research, Vol. 35, No. 6. pp.372-389, 1980.
[6] B. Boashash, Time-Frequency Signal Analysis and Processing – A
Comprehensive Reference, Elsevier Science, Oxford, 2003.
[7] M.Kepesi and L. Weruaga, “Adaptive chirp-based time–frequency
analysis of speech signals”, Speech Communication, 48, pp. 474-492,
2006.
[8] T. Qian, “Intrinsic Mono-components Decomposition of Functions: An
Advance of Fourier Theory”, Math.Meth.Appl.Sci. 33, pp.880-891, 2010.
[9] T. Qian and Y. Wang, “Adaptive Fourier Series – a Variation of Greedy
Algorithm”, Advances in Computational Mathematics, 34, no.3,
pp.279-293, 2011.
[10] D. Gabor, “Theory of Communication”, Journal of the IEE, vol.93,
pp.~429-457, 1946.
[11] T. Qian, Q. H. Chen and L.Q. Li, “Analytic unit quadrature signals with
non-linear phase”, Physica D: Nonlinear Phenomena, 303, 80-87 2005.
[12] T. Qian, “Characterization of boundary values of functions in Hardy
spaces with applications in signal analysis”, Journal of Integral Equations
and Applications, Volume 17, Number 2, pp 159-198, 2005.
[13] T. Qian, “Analytic Signals and Harmonic Measures”, J. Math. Anal.
Appl. 314, pp.526-536, 2006.
[14] T. Qian, L. Zhang and Z. Li, “Algorithm of Adaptive Fourier Transform”,
IEEE Transactions on Signal Processing, vol.59(12),5899-5906,
Dec.,2011.
[15] T. Qian, “Mono-components for decomposition of signals”,
Mathematical Methods in the Applied Sciences, 29, pp. 1187-1198,
2006..
[16] T. Qian, “Boundary Derivatives of the Phases of Inner and Outer
Functions and Applications”, Mathematical Methods in the Applied
Sciences, 32, pp. 253-263, 2009.
[17] T. Qian and E. Wegert, “Optimal Approximation by Blaschke Forms”,
Complex Variables and Elliptic Equations, preprint Available online: 20
Jun 2011.
[18] TIMIT Acoustic-Phonetic Continuous Speech Corpus,
http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC93S
1 (Retrieved on 27/11/2012)</p>
@article{"International Journal of Electrical, Electronic and Communication Sciences:59603", author = "Liming Zhang", title = "A New Time-Frequency Speech Analysis Approach Based On Adaptive Fourier Decomposition", abstract = "In this paper, a new adaptive Fourier decomposition
(AFD) based time-frequency speech analysis approach is proposed.
Given the fact that the fundamental frequency of speech signals often
undergo fluctuation, the classical short-time Fourier transform (STFT)
based spectrogram analysis suffers from the difficulty of window size
selection. AFD is a newly developed signal decomposition theory. It is
designed to deal with time-varying non-stationary signals. Its
outstanding characteristic is to provide instantaneous frequency for
each decomposed component, so the time-frequency analysis becomes
easier. Experiments are conducted based on the sample sentence in
TIMIT Acoustic-Phonetic Continuous Speech Corpus. The results
show that the AFD based time-frequency distribution outperforms the
STFT based one.
", keywords = "Adaptive fourier decomposition, instantaneous
frequency, speech analysis, time-frequency distribution.", volume = "7", number = "7", pages = "889-5", }
