The aim of this paper is to propose an adaptive method for suppressing wideband interferences in spread spectrum (SS) communications. The proposed method is based on the time--frequency representation of the received signal from which the parameters of an adaptive time-varying interference excision filter are estimated. The approach is based on the generalized Wigner--Hough transform as an effective way to estimate the instantaneous frequency of parametric signals embedded in noise. The performance of the proposed approach is evaluated in the presence of linear and sinusoidal FM interferences plus white Gaussian noise in terms of SNR improvement factor and bit error rate (BER).

We introduce a highly manipulable, high fidelity additive sound model capable of representing transient sounds and sounds having significant nonsinusoidal energy. We represent sounds as a collection of bandwidth-enhanced partials having sinusoidal and noise-like characteristics. Partials are defined by a trio of synchronized breakpoint envelopes specifying the time-varying amplitude, center frequency, and noise content. Breakpoints for the partial envelopes are obtained by following ridges on a time-frequency surface computed by the method of reassignment. The Reassigned Bandwidth-Enhanced Model yields greater resolution in time and frequency than conventional additive techniques, and preserves noise-like and transient signals, even in modified reconstruction.

We overview a new non-parametric method for estimating the time-varying spectrum of a non-stationary random process. Our method extends Thomson’s powerful multiple window spectrum estimation scheme to the time-frequency and time-scale planes. Unlike previous extensions of Thomson’s method, we identify and utilize optimally concentrated Hermite window and Morse wavelet functions and develop a statistical test for extracting chirping line components. Examples on synthetic and real-world data illustrate the superior performance of the technique. 2

The topic of this article is the estimation of the colored noise level in audio signals with mixed noise and sinusoidal components. The noise envelope model is based on the assumptions that the envelope varies only slowly with frequency and that the noise amplitudes obey a Rayleigh distribution. The method is an extension of a recently proposed approach of classification of sinusoidal and noise spectral peaks, which takes into account the noise envelope model to improve the detection of sinusoidal peaks. By means of iterative evaluation and adaptation of the noise envelope model, the classification of noise and sinusoidal peaks is iteratively refined until the detected noise peaks are coherently explained by the noise envelope model. Testing examples of nearly white noise and colored noise are demonstrated. 1.

Time-frequency reassignment may be viewed as a refinement of the short-time Fourier transform, in which phase information is used to reduce the smearing of energy associated with the standard spectrogram. However, even given the perceptibly clearer visual representation yielded by the reassignment method in the case of musical signals, the task remains of extracting useful information from it for further processing. To this end it is proposed that time reassignment information be used to help identify musical transients, and that frequency reassignment information be similarly employed as a means of estimating the pitch of musical signal components. To illustrate these ideas, an example is shown in which reassigned time and frequency points are used to segment a monophonic piano melody and locate the partials of its individual notes. Lastly, the potential role of reassignment in the overall framework of music transcription is described, and several areas are detailed for future study.

In extending previous work on detecting transient spectral peaks we here investigate into the distinction between sinusoidal and noise components by means of classification of spectral peaks. The classification is based on descriptors derived from properties related to time-frequency distributions. In contrast to existing methods, the descriptors are designed to properly deal with non-stationary sinusoids, which considerably increases the range of applications. The experimental investigation shows superior classification results compared to the standard correlation-based approach. 1

In this paper, we propose a method for selecting time-frequency distributions appropriate for given learning tasks. It is based on a criterion that has recently emerged from the machine learning literature: the kernel-target alignment. This criterion makes possible to find the optimal representation for a given classification problem without designing the classifier itself. Some possible applications of our framework are discussed. The first one provides a computationally attractive way of adjusting the free parameters of a distribution to improve classification performance. The second one is related to the selection, from a set of candidates, of the distribution that best facilitates a classification task. The last one addresses the problem of optimally combining several distributions.

This paper was published in the Proceedings of the Society of Exploration Geophysics 67th Annual Meeting, November 2-7, 1997, Dallas, TX. SUMMARY

Linear time-varying (LTV) channels are often encountered in mobile communications but, as opposed to the linear time-invariant (LTI) channels case, there is no a well established theory for computing the channel capacity, or providing simple bounds to the maximum information rate based only on the channel impulse response, or predicting the structure of the channel eigenfunctions. In this paper, we provide: i) a method for computing the mutual information between blocks of transmitted and received sequences, for any finite block length; ii) the optimal precoding (decoding) strategy to achieve the maximum information rate; iii) an upper bound for the channel capacity based only on the channel time-varying transfer function; iv) a time-frequency representation of the channel eigenfunctions, revealing a rather intriguing, but nonetheless intuitively justifiable, bubble structure.

29.47> Pour r'eallouer le spectrogramme associ'ee `a la transform'ee de Fourier `a Court Terme (FCT) de fenetre h F h (x; t; ) = Z x(ø )h (t \Gamma ø )e 2ßi(t\Gammaø) dø = Z x(t \Gamma ø )h (ø )e 2ßiø dø = Z X(¸)H ( \Gamma ¸)e 2ßi¸t d¸ il est n'ecessaire d'introduire deux autres FCT du signal de fenetre t:h(t) et dh=dt. R'eallouer, c'est alors d'eplacer la valeur du spectrogramme jF h (x; t;<F29.