On representing signals using only timing information.

It is well known that only a special class of bandpass signals, called real-zero (RZ) signals can be uniquely represented (up to a scale factor) by their zero crossings, i.e., the time instants at which the signals change their sign. However, it is possible to invertibly map arbitrary bandpass signals into RZ signals, thereby, implicitly represent the bandpass signal using the mapped RZ signal's zero crossings. This mapping is known as real-zero conversion (RZC). In this paper a class of novel signal-adaptive RZC algorithms is proposed. Specifically, algorithms that are analogs of well-known adaptive filtering methods to convert an arbitrary bandpass signal into other signals, whose zero crossings contain sufficient information to represent the bandpass signal's phase and envelope are presented. Since the proposed zero crossings are not those of the original signal, but only indirectly related to it, they are called hidden or covert zero crossings (CoZeCs). The CoZeCs-based representations are developed first for analytic signals, and then extended to real-valued signals. Finally, the proposed algorithms are used to represent synthetic signals and speech signals processed through an analysis filter bank, and it is shown that they can be reconstructed given the CoZeCs. This signal representation has potential in many speech applications.

[1]  T. V. Sreenivas,et al.  Zero-crossing based spectral analysis and SVD spectral analysis for formant frequency estimation in noise , 1992, IEEE Trans. Signal Process..

[2]  Yair Shoham,et al.  New directions in subband coding , 1988, IEEE J. Sel. Areas Commun..

[3]  Shlomo Shamai,et al.  On the duality of time and frequency domain signal reconstruction from partial information , 1985, IEEE Trans. Acoust. Speech Signal Process..

[4]  S. Kay Fundamentals of statistical signal processing: estimation theory , 1993 .

[5]  G. Kubin,et al.  Multiple-description coding (MDC) of speech with an invertible auditory model , 1999, 1999 IEEE Workshop on Speech Coding Proceedings. Model, Coders, and Error Criteria (Cat. No.99EX351).

[6]  F. Itakura Line spectrum representation of linear predictor coefficients of speech signals , 1975 .

[7]  J. Allen,et al.  Cochlear modeling , 1985, IEEE ASSP Magazine.

[8]  Alan V. Oppenheim,et al.  Discrete-Time Signal Pro-cessing , 1989 .

[9]  Piet M. T. Broersen,et al.  On the statistical properties of line spectrum pairs , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.

[10]  A. J. Jerri The Shannon sampling theorem—Its various extensions and applications: A tutorial review , 1977, Proceedings of the IEEE.

[11]  Manfred R. Schroeder,et al.  Code-excited linear prediction(CELP): High-quality speech at very low bit rates , 1985, ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[12]  J. Makhoul,et al.  Linear prediction: A tutorial review , 1975, Proceedings of the IEEE.

[13]  Yehoshua Y. Zeevi,et al.  Image representation by zero and sine-wave crossings , 1987 .

[14]  A. J. Jerri Correction to "The Shannon sampling theorem—Its various extensions and applications: A tutorial review" , 1979 .

[15]  Kuansan Wang,et al.  Auditory representations of acoustic signals , 1992, IEEE Trans. Inf. Theory.

[16]  A. Oppenheim,et al.  Signal synthesis and reconstruction from partial Fourier-domain information , 1983 .

[17]  B. Logan Information in the zero crossings of bandpass signals , 1977, The Bell System Technical Journal.

[18]  S. Seneff A joint synchrony/mean-rate model of auditory speech processing , 1990 .

[19]  Gernot Kubin,et al.  On speech coding in a perceptual domain , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[20]  Annette Dolphin Neural Codes and Distributed Representations: Foundations of Neural Computation , 2000 .

[21]  H. Voelcker,et al.  Clipping and Signal Determinism: Two Algorithms Requiring Validation , 1973, IEEE Trans. Commun..

[22]  A. Papoulis,et al.  The Fourier Integral and Its Applications , 1963 .

[23]  Biing-Hwang Juang,et al.  Line spectrum pair (LSP) and speech data compression , 1984, ICASSP.

[24]  H. Voelcker Toward a unified theory of modulation part I: Phase-envelope relationships , 1966 .

[25]  Mark A. Poletti,et al.  The homomorphic analytic signal , 1997, IEEE Trans. Signal Process..

[26]  A. Requicha,et al.  The zeros of entire functions: Theory and engineering applications , 1980, Proceedings of the IEEE.

[27]  Bernard C. Picinbono,et al.  On instantaneous amplitude and phase of signals , 1997, IEEE Trans. Signal Process..

[28]  Steven Kay,et al.  Modern Spectral Estimation: Theory and Application , 1988 .

[29]  Israel Bar-David,et al.  An Implicit Sampling Theorem for Bounded Bandlimited Functions , 1974, Inf. Control..

[30]  R. Kumaresan,et al.  Model-based approach to envelope and positive instantaneous frequency estimation of signals with speech applications , 1999 .

[31]  R. Patterson,et al.  Complex Sounds and Auditory Images , 1992 .