Complementary envelope estimation for frequency-modulated random signals

The Hilbert envelope is a well-known indicator for amplitude modulation in subband time series. However, frequency modulation estimators typically impose temporal smoothness constraints that limit their general use for stochastic signals. We introduce the complementary envelope as a direct indicator of stochastic phase coherence and frequency modulation. Rooted in the second-order statistics of randomly-phased sinusoids, the complementary envelope is distinct from the Hilbert envelope, and can be estimated easily and separately. We propose a new complementary envelope estimator based on modified multitaper spectral analysis, and use it to reveal previously unseen FM-like behavior in ship propeller noise.

[1]  Peter J. Schreier Polarization Ellipse Analysis of Nonstationary Random Signals , 2008, IEEE Transactions on Signal Processing.

[2]  James L. Massey,et al.  Proper complex random processes with applications to information theory , 1993, IEEE Trans. Inf. Theory.

[3]  Pascal Chevalier,et al.  Widely linear estimation with complex data , 1995, IEEE Trans. Signal Process..

[4]  O Ghitza,et al.  On the upper cutoff frequency of the auditory critical-band envelope detectors in the context of speech perception. , 2001, The Journal of the Acoustical Society of America.

[5]  Thomas F. Quatieri,et al.  Speech analysis/Synthesis based on a sinusoidal representation , 1986, IEEE Trans. Acoust. Speech Signal Process..

[6]  Malcolm Slaney,et al.  Solving Demodulation as an Optimization Problem , 2010, IEEE Transactions on Audio, Speech, and Language Processing.

[7]  Louis L. Scharf,et al.  Detection and estimation of improper complex random signals , 2005, IEEE Transactions on Information Theory.

[8]  Esa Ollila,et al.  On the Circularity of a Complex Random Variable , 2008, IEEE Signal Processing Letters.

[9]  P. Loughlin,et al.  On the amplitude‐ and frequency‐modulation decomposition of signals , 1996 .

[10]  Pascal Bondon,et al.  Second-order statistics of complex signals , 1997, IEEE Trans. Signal Process..

[11]  R V Shannon,et al.  Speech Recognition with Primarily Temporal Cues , 1995, Science.

[12]  Louis L. Scharf,et al.  Stochastic time-frequency analysis using the analytic signal: why the complementary distribution matters , 2003, IEEE Trans. Signal Process..

[13]  Hynek Hermansky,et al.  Temporal envelope compensation for robust phoneme recognition using modulation spectrum. , 2010, The Journal of the Acoustical Society of America.

[14]  D. Thomson,et al.  Spectrum estimation and harmonic analysis , 1982, Proceedings of the IEEE.

[15]  Richard E. Turner,et al.  Probabilistic amplitude and frequency demodulation , 2011, NIPS.

[16]  Michel Loève,et al.  Probability Theory I , 1977 .

[17]  Les E. Atlas,et al.  Time-Frequency Coherent Modulation Filtering of Nonstationary Signals , 2009, IEEE Transactions on Signal Processing.

[18]  R. Plomp,et al.  Effect of temporal envelope smearing on speech reception. , 1994, The Journal of the Acoustical Society of America.

[19]  Louis L. Scharf,et al.  Second-order analysis of improper complex random vectors and processes , 2003, IEEE Trans. Signal Process..

[20]  S. Rice Mathematical analysis of random noise , 1944 .

[21]  Leon Cohen,et al.  On an ambiguity in the definition of the amplitude and phase of a signal , 1999, Signal Process..

[22]  J. G. Lourens Passive sonar ML estimator for ship propeller speed , 1997, Proceedings of the 1997 South African Symposium on Communications and Signal Processing. COMSIG '97.

[23]  William M. Brown,et al.  Conjugate linear filtering , 1969, IEEE Trans. Inf. Theory.

[24]  E. Bedrosian A Product Theorem for Hilbert Transforms , 1963 .