Showing papers on "Spectrogram published in 1992"

Alias-free generalized discrete-time time-frequency distributions

Abstract: A definition of generalized discrete-time time-frequency distribution that utilizes all of the outer product terms from a data sequence, so that one can avoid aliasing, is introduced. The new approach provides (1) proper implementation of the discrete-time spectrogram, (2) correct evaluation of the instantaneous frequency of the underlying continuous-time signal, and (3) correct frequency marginal. The formulation provides a unified framework for implementing members of Cohen's class, which was formulated in the continuous-time domain. Some requirements for the discrete-time kernel in the new approach are discussed in association with desirable distribution properties. Some experimental results are provided to illustrate the features of the proposed method. >

Classification of whale and ice sounds with a cochlear model

Abstract: The authors analyze a cochlear model data representation for use in classifying acoustic transient signals. Comparisons of the cochlear model, short time Fourier transform, and wavelet transform from a filter bank perspective are made. The cochleagram and the spectrogram are used to classify a set of bowhead whales and ice sounds from the Arctic. >

Time-frequency transforms: a new approach to first heart sound frequency dynamics

Abstract: The binomial joint time-frequency transform is used to test the hypothesis that first heart sound frequency rises during the isovolumic contraction period. Cardiac vibrations were recorded from eight open-chest dogs using an ultralight accelerometer cemented directly to the epicardium of the anterior left ventricle. Three characteristic time-frequency spectral patterns were evident in the animals investigated: (1) a frequency component that rose from approximately 40-140 Hz in a 30-50-ms interval immediately following the ECG R-wave, (2) a slowly varying or static frequency of 60-100 Hz beginning midway through the isovolumic contraction period, and (3) broadband peaks occurring at the time of the Ia and Ib high frequency components. The binomial transform provided much better resolution than the spectrograph or spectrogram. By revealing the onset and dynamics of first heart sound frequencies, time-frequency transforms may allow mechanical assessment of individual cardiac structures. >

Method and apparatus for processing signal data to form an envelope on line

Abstract: An apparatus for generating a velocity spectrogram is described. The apparatus includes a transducer which is positionable within a vessel. The transducer receives a time-varying Doppler signal which contains information related to fluid velocity values within the vessel. A Fourier transformation device processes the time-varying Doppler signal to generate a sequence of spectra. Each spectrum corresponds to a segment of the time-varying Doppler signal and defines a set of velocities and their corresponding spectral values. A device is used to identify the instantaneous spectral peak velocity within each spectra. The peak velocity corresponds to the highest velocity within a spectra which has a spectral value above a defined threshold value which is related to the background noise level. The sequence of spectral peak velocities is plotted on a visual interface device to form an instantaneous spectral peak velocity waveform.

Shape from periodic texture using the spectrogram

Abstract: It is shown how local spatial image frequency is related to the surface normal of a textured surface. It is found that the Fourier power spectra of any two similarly textured patches on a plane are approximately related to each other by an affine transformation. The transformation parameters are a function of the plane's surface normal. This relationship is used as the basis of an algorithm for finding surface normals of textured shapes using the spectrogram, which is one type of local spatial frequency representation. The relationship is validated by testing the algorithm on real textures. By analyzing shape and texture in terms of the local spatial frequency representation, the advantages of the representation for the shape-from-texture problem can be exploited. Specifically, the algorithm requires no feature detection and can give correct results even when the texture is aliased. >

An information-theoretic approach to positive time-frequency distributions

Abstract: The principle of minimum cross entropy (MCE) is used to generate positive distributions in the Cohen-Posch (1985) class of proper time-frequency distributions (TFDs). The MCE-TFDs are not only intuitively satisfying, but they also yield the correct marginals, have strong finite support, and are everywhere nonnegative. The usual cross-term artifacts that hinder interpretation of other time-frequency representations (most notably, the Wigner-Ville) are not a problem with the MCE-TFDs. Examples of speech and chirps are given and compared to the spectrogram. An interesting observation in the case of speech is that spectrograms more closely resemble time-conditional-frequency distributions (narrowband) or frequency-conditional-time distributions (wideband) than they do joint time-frequency distributions. >

Some properties of the generalized time frequency representation with cone-shaped kernel

Abstract: The cone-shaped kernel generalized time-frequency representation (GTFR) of Zhao, Atlas, and Marks (ZAM) has been shown empirically to generate quite good time frequency representation in comparison to other approaches. The authors analyze some specific properties of this GTFR and compare them to other TFRs. Asymptotically, the GTFR is shown to produce results identical to that of the spectrogram for stationary signals. Interference terms normally present in many GTFRs are shown to be attenuated drastically by the use of the ZAM-GTFR. The ability of the ZAM-GTFR to track frequency hopping is shown to be close to that of the Wigner distribution. When a signal is subjected to white noise, the ZAM-GTFR produces an unbiased estimate of the ZAM-GTFR of the signal without noise. In many other GTFRs, the power spectral density of the noise is superimposed on the GTFR of the signal. It is also shown that, in discrete form, the ZAM-GTFR is generally invertible. >

Method and apparatus for processing signals to extract narrow bandwidth features

Abstract: A method and apparatus for processing a set of signals to identify narrow bandwidth features of the signals, and optionally to process the signals further to extract information about the identified narrow-band features. The invention processes a set of input signal frames (a two-dimensional pixel array) to generate a narrow-band feature signal (also a two-dimensional pixel array) from which narrow-band features of the input signal frames can be efficiently, automatically, and unambiguously identified. In a class of preferred embodiments, the input signal frames are the power spectra of a set of sequentially measured signals. Thus, the set of input signal frames is a "spectrogram," comprising rows and columns of pixels (with row indices representing time, and column indices representing frequency). Alternatively, the input signal frames represent a data array of another type, such as a correlogram or a sequence of images. In a class of embodiments, the input signal frames are processed to identify narrow-band pixels (the subset of input signal frame pixels which represent narrow-band energy, or narrow-band processes). Then, the identified narrow-band pixels (which can be displayed as a narrow-band pixel array) undergo "feature extraction" processing to generate the output narrow-band feature signal (which can be displayed as the output image). The narrow-band feature signal can be further processed to determine the center frequency, bandwidth, and amplitude of one or more of its narrow-band features.

Combined multiresolution (wide-band/narrow-band) spectrogram

Abstract: Existing speech spectrograms-the wideband spectrogram and the narrowband spectrogram-are either deficient in time or frequency resolution. The authors present a method to combine the two spectrograms by evaluating the geometric mean of the corresponding short-time Fourier transform magnitudes. The combined spectrogram preserves the desirable visual features of the originals. >

An image processing approach to frequency tracking (application to sonar data)

Abstract: A method for characterizing a signal's narrowband content is presented. The scenario of interest is one in which the narrowband components are nonstationary, but persist for long periods of time, such as is found in many passive sonar and vibration analysis applications. This so-called frequency tracking problem-generally treated as a one-dimensional problem-is seen to reduce to that of extracting line structures from gray-scale images called spectrograms. Accordingly, the frequency tracking method presented is simply a line extraction technique based on standard image processing and machine vision methods and tuned to the type of line features and noise background present in spectrograms. Initial experiments using real sonar data indicate that the method outperforms currently used methods and has comparable computational cost. >

Mechanism of the cross-terms in spectrograms

Abstract: Using the ambiguity domain interpretation, the authors explore the mechanism of the cross-terms (or interferences) in spectrograms. They emphasize that the spectrogram does, in general, have cross-terms. Depending on the signal structure and the window used, the cross-terms may sometimes be conspicuous, reduced, or completely eliminated. The cross-term suppression in spectrograms is governed by the signal-theoretic version of the uncertainty principle. Some experimental results are provided to illustrate the cross-term mechanism in spectrograms. >

Optimal time-window duration for computing time/frequency representations of normal phonocardiograms in dogs.

Abstract: The optimal duration of the time-window used to compute the time-frequency representation (spectrogram) of the phonocardiogram was studied in four dogs by using intracardiac and thoracic measurements of the PCG. The power and cross-spectrograms of the intracardiac and thoracic PCGs were computed using a fast Fourier transform algorithm and a sine-cosine window with 10 per cent decaying functions. A coherence spectrogram was also computed for each dog to study the linear relationship between the two signals and determine the optimal time-window duration. Results show that the optimal range of the time-window duration is between 16 and 32 ms. A time-window shorter than 16ms spreads out low-frequency components into the higher frequencies and generates a spectrographic representation with poor frequency resolution (≥62·5 Hz). A window larger than 32 ms increases the frequency resolution but smears the spectrographic representation of the signal in the time domain and thus cannot correctly reflect the time-varying properties of the signal. In both cases, the amplitude of the coherence function computed between the left ventricular and the thoracic phonocardiograms is overestimated.

Automatic feature extraction from spectrograms for acoustic-phonetic analysis

Abstract: Proposes a new approach for automatic feature extraction from spectrograms, which is an essential component of acoustic-phonetic analysis in automatic continuous speech recognition. The method comprised four levels: segmentation, pattern classification, feature recognition and labelling, and a post-processor. There were three types of patterns: fuzzy, formant and silence. The extracted features included voice bar, stripes, cut-off and transitions of the first four formants. Some techniques are presented, such as two special distortion functions used in segmentation, and a peak-iterate function to detect the stripes feature. This software has been implemented as part of a speech knowledge interface, which was an expert system for speech analysis for speaker-independent, continuous speech recognition. It has been tested with a set of data chosen from a spectrogram database; the correct detection rate for most features was over 89%, and in some cases was as high as 98%. >

Radar target discrimination by spectrum analysis

Abstract: A method for distinguishing between a target and clutter analyzes frequency components of returned wave energy to detect target energy characterized by being present in a narrow range of frequencies, by increasing in the range over time, or by remaining substantially in the range over time. The method utilizes time sequential spectra of the returned energy. The spectra may be signals from a plurality of band pass filters or may be a spectrogram. The energy in adjacent band pass signals and spectra frequencies are correlated to detect energy in a narrow range of frequencies. Differences in successive spectra are integrated to detect increasing energy in a range of frequencies. An energy peak detected in a narrow range by correlation is integrated to detect that the peak remains in the range. The method is adapted for use with a network having inputs receiving successive samples of the returned energy and having outputs individually connected to the inputs through multiplier elements providing selectable factors, a summing element, and a selectable and generally sigmoidal activation function.

Window matching in the time-frequency plane and the adaptive spectrogram

Abstract: Aspects of generating spectrograms which may be used in the preliminary steps of time-frequency analysis are discussed. It is shown that the major problem with the application of the spectrogram is its nonunique nature, i.e., the use of an arbitrary window for analysis. The concept of window matching is introduced, whereby the selection of the window used for analysis depends on the instantaneous signal characteristics at the given time-frequency point under analysis. Generalized instantaneous parameters are derived and subsequently used to demonstrate how the window matching may be applied. An adaptive window-matched spectrogram is generated. Practical data results are given. >

An analog cochlear model for multiresolution speech analysis

Abstract: Uniform bandwidth spectral analysis methods, such as spectrogram, are not suitable for rapidly changing broadband speech signals since the tradeoff between temporal and spectral resolutions is fixed for all frequencies. Described here is the signal representation by a model of the auditory periphery, based on neurophysiological data and realized in analog integrated circuitry. The main modules in the model are the basilar membrane filter bank and the reservoir model of hair cells/synapses [earlier version described in Liu et al., IEEE Trans. Neural Nets 3, 477–487 (1992)]. The instantaneous firing rates of the auditory‐nerve fibers are modeled. Although nonconstant Q conditions in the cochlear filter are necessary in order to match the model output to the neural data, the model does exhibit properties of a wavelet analysis. Temporal analysis of the model output yields accurate determination of both time and frequency of each component in synthetic and natural speech signals. Finally, the low‐power large‐s...

Fast computation of the Wigner distribution for finite-length signals

Abstract: L. Cohen's class (1989) of time-frequency distributions (TFDs), which includes the spectrogram and Wigner distribution (WD), has been widely used to analyze a variety of signals, including EMGs, EEGs, sonar data and speech. The WD is noted for its ability to localize mono-component signals in time and frequency, and, other than the spectrogram, is perhaps the most often-used TFD. An approach for the evaluation of the discrete-time WD (DTWD) of a finite-length signal which requires 25% less computation time than traditional schemes is introduced. Traditional fast DTWD algorithms rely on the fast Fourier transform (FFT), which shifts autocorrelation slices (decimates in frequency) in order to minimize multiplications. The approach uses a decimation scheme which shifts the signal rather than autocorrelation slices, effectively moving blocks of the autocorrelation, so that the resulting twiddle multiplications are reduced. >

A spectral magnitude analysis theorem and applications

Abstract: Spectral magnitude analysis can be performed with identical results by computing the short-time Fourier transform magnitude ( mod STFT mod ) of a real signal, or by analyzing the signal with a filter/detector (F/D) bank. In the latter approach, analysis is performed by a bank of bandpass filters, each of which is followed by a detector consisting of a square law device. The equivalence between mod STFT mod and F/D bank analysis is extended to complex signals. New applications such as complex signal reconstruction from spectral magnitude and pulse-Doppler radar signal processors are discussed. A new equivalence between wavelet analysis and the generalized STFT is presented, and it is shown that speech spectrograms are not equivalent to the mod STFT mod . >

Recent advances in sonar target classification

Abstract: The discrimination between metallic and nonmetallic targets submerged in water or even embedded in the sea bottom is of some concern for present sonar technology. It can be achieved by processing the backscattered sonar echo from monostatic measurements. Information on the target material and inner structure (rather than merely the outer shape) is obtained only if resonances of the target are excited by the sound pulse. The 'Resonance Scattering Theory' (RST) proves an 'acoustical spectrogram' to characterize the target just as optical spectra do with atoms, molecules, etc. This analogy applies, however, to underwater objects of very small dimensions only; with realistic, full size targets typically very few resonances can be identified due to the strong radiation damping which grows with increasing frequency. It causes wide overlap of the individual resonances so that spectral analysis does not yield a reliable decomposition in the presence of an even small amount of random noise. This has been verified by measurements in a fresh water tank and in a lake. Successful signal processing turns out to be possible, however, by combining resonance excitation of the targets with signal processing in a neural network classifier. Results obtained with power spectral or time series input data to the neural net will be presented. Cylindrical and (hemi) spherical steel shells and several stones have been used as targets. The results obtained so far are quite promising so that a reliable classification is likely to be possible even under more severe operating conditions.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

On the window selection and the cross terms that exist in the magnitude squared distribution of the short time Fourier transform

Abstract: The short time Fourier transform (STFT), a time-frequency representation, is linear by definition. However, the magnitude squared distribution of the STFT (the spectrogram) which signal processors often use to represent a signal is non-linear by definition. Therefore, in general, there exist cross terms in the spectrogram of a multi-component signal. The existence of these depends on (a) the nature of a multi-component signal, (b) the choice of the STFT analysis window and (c) the window length selection. This paper studies the effect of (i) the choice of the STFT analysis window, and (ii) the nature of signals to be analyzed, on the cross terms, in detail with representative examples. >

Application of the modulation model to speech recognition

Abstract: A novel method of characterizing speech signals is presented in which the signal is modeled in terms of the mean radian frequency, the amplitude modulating function and the frequency modulating function of each frequency domain component (i.e., formants). This model defines the component structure and represents the local characteristics of the component and its bandwidth. A vowel classification task is presented where a distance metric of the relative modulation functions is used. This technique has been found to be robust to inter- and intraspeaker variability. >

Time-frequency localization operators

Abstract: A technique for producing signals whose energy is concentrated in a given region of the time-frequency plane is examined. The degree to which a given signal is concentrated in a region is measured by integrating its time-frequency distribution over the region. This method has been used for time-varying filtering. Localization operators based on the Wigner distribution and spectrogram are studied. Estimates for the eigenvalue decay and the smoothness and decay of the eigenfunctions are presented. >

Multiresolution speech analysis with an analog cochlear model

Abstract: The limitations of uniform bandwidth analysis methods (such as the spectrogram) for broadband signals when both temporal and spectral resolution are important are discussed. Signal representation by an auditory periphery model based on neurophysiological data is described. The model exhibits wavelet-like properties. The temporal analysis of the response of the model yields accurate time and frequency determination for each spectral component in a dynamic broadband signal. The signal processing capability of the model is demonstrated with natural speech input. The model has been realized in silicon as a real-time, low-power hardware signal processor. >

5 - Analyse spectrale locale de l'image par transformation de Wigner-Ville

Abstract: Local spectrum analysis is an interesting method to extract pertinent features of an image . This paper proposes a new local spectrum analysis method allowing to accurately characterize the local spatial frequency content of an image. It is based on the use of the two-dimensional Wigner-Ville distribution (2D WVD), which permits to separately control spatial and frequential analysis resolutions. The application of this method to texture feature extraction and discrimination is illustrated, and a comparison with the classical 2D spectrogram method is also given.

The Wigner Distribution in the Doppler Sonography

Abstract: In the field of Doppler sonography one has to carry out some spectrum analysis generally. Today the Fourier analysis is the most used method. The spectrogram S(ω,t) is currently the standard method for the investigation of time-varying signals s(t): $$ S(\omega ,t) = \int {s(\tau )} h(\tau - t){e^{ - j\omega \tau }}d{\tau ^2} $$ (1) with the time window h(t). This window is chosen to obtain short segments of the signal without substantial changes of parameters. If we can obtain such segments the spectrogram gives a good time-frequency representation of the signal energy. However, there exist a large variety of signals whose spectra are changing so rapidly that finding a time window for which the signal is more or less stationary is very difficult. Additional to this problem the instantaneous spectra of the signal at distinct times are required in some cases. In the spectrogram the time window can not be shorted infinitly to solve the mentioned problems.