A simple and reliable method for tissue embedding for Mohs micrographic surgery is described. This technique reduces the number of frozen sections necessary to evaluate a complete margin and can easily be adapted to the office setting without the need of special equipment.
A new time-frequency display is constructed based on the phase of the running short-time Fourier transform, specifically the distribution of its time derivative. Typical results are given for speech, indicating more precise location of formants than is usual for the spectrogram.Some insights as to the pertinent structural aspects of the speech signal can clearly be gained from study of the auditory system in humans and other mammals. The bulk of experimental evidence indicates that the ear performs some sort of running short-time spectral analysis of the acoustic waveform. Recent work has suggested moreover that the information encoded in neural activity consists of more than a representation of spectral amplitude: it appears that phase-locking of individual unit firings to the local basilar-membrane motion is maintained up to several kHz. Recent simulation studies, e.g., [L82,L84] have incorporated this behavior in an effort to determine what characteristics of the signal might be preserved in transmission through the peripheral auditory pathway. There is now increasing justification for the view that the spectral decomposition occurring in the ear serves merely to isolate individual frequencylocalized components of the signal, with further processing done essentially in the time domain. A highlydeveloped instance of such an analysis mode in a mammalian auditory system has recently been pointed out in the case of bat sonar {SBzfl.The implications of this view for artificial speech recognition provide the motivation for this work. One is led to ask whether, in place of the usual time-varying powerspectrum description of the input signal, a better choice for the initial processing step would be one taking into account the phase structure of the speech waveform.This need not involve explicit modeling of the ear, since if the encoding of an acoustic waveform into a neural firing pattern can be understood in terms of an underlying signal-space transformation -in the sense that the displacement of the basilar membrane vs. time can be seen as essentially a running short-time Fourier transform of the acoustic wave -then a variety of conceivable implementations exists from which one may choose that best suited to given means and requirements. Rather than attempt here to deduce such a transformation from first principles and available evidence, we adopt a much more modest goal: to show a new display interpretation of the running short-time Fourier transform (RSTFT) suggestive of, and inspired by. the image of a group of adjacent nerve fibers phaselocked to a single frequency component of the acoustic signal.It is well known that the RSTFT can be interpreted in terms of simultaneous outputs of a bank of band-pass filters with successively-offset center frequencies, all having the given signal waveform as input. If the transform of x(t) is defhied aswhere w(r) is a real-valued, non-negative symmetrical window function, nonzero only for vi C Ff2 (e.g., the Hann window), this can be represented as the output of the tn-th fil...
The instantaneous-frequency distribution (IFD) of the outputs of a filter bank of overlapping band-pass channels (or the equivalent D F T implementation) has been proposed [l] as a short-time spectral measure useful for representing speech. For application of the IFD in conventional tasks such as word recognition, an appropriate vectorization is needed on which a useful distance function can be defined. A number of recent studies have shown the cepstral representation to be generally most effective in terms of recognition accuracy. We propose a "pseudo-cepstral" format which treats the IFD as if it were a log-power spectrum, with a matrixweighted squared-Euclidean distance function. Comparative results are given for two forms of IFD as well as two conventional cepstral transforms, in a small-scale word-recognition task with added white Gaussian noise. In addition, a statistic is derived which is highly correlated with recognition error rates, but requires far less computation than is involved in an actual recognition trial.The 'front end" acoustic-to-parameter-vector transformation is a critical part of any speech-processing system, in regard t o tolerance to degradation in the quality of the speech signal. The more immune the input transformation can be made to common forms of degradation (additive noise, spectral "tilt", etc.), the more reliable will be the results of subsequent processing or, alternately, the less need to apply higher-level linguistic or other contextual constraints in order t o realize a particular level of performance.As of the present state of knowledge, the best 'front-end" stage by the above criterion still appears to be the human ear, although its intrinsic capabilities (apart from higher-level processing in auditory and linguistic areas of the brain) are not known for certain. In a previous work 1 1 1 we argued for a short-time, band-pass filter-bank description of the instantaneous-frequency content* of the signal, which we referred to as the instantaneous-frequency distribution (IFD), as possibly underlying the transformation performed by the ear as currently understood. Since each channel of the IFD filter bank can be seen as an idealized FM receiver, known results from FM theory can be applied, suggesting amplitude independence (hence resistance to spectral distortion or "tilt") and superior noise immunity by comparison to conventional transforms such as LPC.One problem to be solved, prior t o verifying these and other anticipated properties, is the formulation of an appropriate reduction of the IFD to a vector format involving on the order of ten coordinates, and a "frame interval" between successive vectors on the order of 10 to 20 msec. Since the next stage of overall processing is likely * For some related analyses see [2], [3].to involve a form of spectral matching to a stored template, the vector format must allow for definition of a distance function between 'template" and 'test" vectors which yields decision results as nearly correct as possible. A representative example of ...
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