Showing papers on "Spectrogram published in 1984"

Application Of The Radon Transform To Optical Production Of The Wigner Distribution Function

Abstract: The Wigner distribution function (WDF), a simultaneous coordinate and frequency representation of a signal, has properties useful in pattern recognition. Because the WDF is computationally demanding, its use is not usually appropriate in digital processing. Optical schemes have been developed to compute the WDF for one-dimensional (1 -D) signals, often using acousto-optic signal transducers. Some recent work has demonstrated the computation of two-dimensional (2-D) slices of the four-dimensional (4-D) WDF of a 2-D input transparency. In this latter case, the required 2-D Fourier transformation is performed by coherent optics. We demonstrate that computation of the WDF of real 2-D signals is susceptible to Radon transform solution. The 2-D operation is reduced to a series of 1 -D operations on the line-integral projections. The required projection data are produced optically, and the Fourier transformation is performed by efficient 1 -D processors (surface acoustic wave filters) by means of the chirp-transform algorithm. The resultant output gives 1 -D slices through the 4-D WDF nearly in real time, and the computation is not restricted to coherently illuminated transparencies. This approach may be useful in distinguishing patterns with known texture direction. The optical setup is easily modified to produce the cross-Wigner distribution function, a special case of the complex, or windowed, spectrogram.

Application of the Radon transform to optical production of the Wigner distribution function

Abstract: The Wigner distribution function (WDF), a simultaneous coordinate and frequency representation of a signal, has properties useful in pattern recognition. Because the WDF is computationally demanding, its use is not usually appropriate in digital processing. Optical schemes have been developed to compute the WDF for one-dimensional (1 -D) signals, often using acousto-optic signal transducers. Some recent work has demonstrated the computation of two-dimensional (2-D) slices of the four-dimensional (4-D) WDF of a 2-D input transparency. In this latter case, the required 2-D Fourier transformation is performed by coherent optics. We demonstrate that computation of the WDF of real 2-D signals is susceptible to Radon transform solution. The 2-D operation is reduced to a series of 1 -D operations on the line-integral projections. The required projection data are produced optically, and the Fourier transformation is performed by efficient 1 -D processors (surface acoustic wave filters) by means of the chirp-transform algorithm. The resultant output gives 1 -D slices through the 4-D WDF nearly in real time, and the computation is not restricted to coherently illuminated transparencies. This approach may be useful in distinguishing patterns with known texture direction. The optical setup is easily modified to produce the cross-Wigner distribution function, a special case of the complex, or windowed, spectrogram.

An expert system for the automatic reading of French spectrograms

Abstract: We report on a long term project on the phonetic decoding of continuous speech, within the framework of an expert system. Spectrogram reading is the field of expertise under study, although it is clear that an automatic phonetic decoder should not be in the form of an expert system. We expect major improvements in the recognition rate when expert's rules will be incorporated into existing systems. We describe here both the method used to retrieve the human expertise and results by an experienced spectrogram reader on 50 phonetically-balanced sentences. We also sketch various ways in which the expert rules can be validated through an "inference engine" and actually used in a system. Finally, we discuss the impact this approach has on the design.

Description of a color spectrogram

Abstract: We define a color spectrogram which retains all the information of the standard spectrogram, and which encodes information about the shape of each short‐time spectrum into the chromaticity with which that spectrum is illuminated. The chromaticity for each spectrum is derived by interpreting each spectrum as an energy distribution in the visible light frequencies.

Microprocessor-based speech processing system.

Abstract: Durational measurements of frication, aspiration, prevoicing, and voice onset are often difficult to perform from the spectrogram, and the resolution is limited to about 5 ms. In many instances, a ...

Sequential noise spectral shaping in ADPCM

Abstract: The output speech from a fixed-tap differential pulse code modulation system with adaptive quantization and adaptive noise spectral shaping (NSS) is compared for block-adaptive and sequentially adaptive NSS filters. Block-adaptive systems incorporate a delay which can build up in analog communications systems and cause echoing problems. The buildup of delay can be eliminated by implementing the sequentially adaptive Kalman algorithm in the NSS filter. Simulations are performed for 4-, 8-, and 16-level quantizers with fourth- and ninth-order Kalman adaptation of the NSS filter. A block-adaptive system is implemented as a reference. Subjective listening tests and spectrograms show the Kalman algorithm to be a viable alternative to the block-adaptive algorithms. Signal-to-noise ratios are also given.

Spectrograms and generalized spectrograms for classification of random processes

Abstract: A maximum likelihood (ML) classifier for discriminating between nonstationary Gaussian time series can be implemented by correlating the data spectrogram with templates that are constructed from ensemble average reference spectrograms. The time window used to synthesize the spectrograms must have a duration that is longer than the decorrelation time of the data in the neighborhood of the window. If the data time series exhibits significant nonstationarity within this decorrelation time, Karhunen-Loeve (K-L) basis functions should ideally be used to construct a generalized spectrogram, rather than using a standard spectrogram constructed with the usual sinusoidal basis functions. Utilization of a standard spectrogram imposes forced, pseudo-stationarity by approximating the autocovariance function of the data by the short-time autocorrelation function. This forced stationarity is routinely used to obtain linear prediction coefficients (LPC). When signal to interference ratio (SNR) is large, the templates that are used to classify a data spectrogram are sensitive to differences in the locations of nulls or zeroes in the expected signal spectrograms from different data classes. This null sensitivity seems to imply that peak-oriented models of random processes, e.g. , the all pole representation that is associated with LPC, are suboptimum for ML classification under high SNR conditions. Compensation for time warping is especially necessary if window durations are data dependent. Spectrogram implementation of the ML classifier yields a new similarity index for time warp compensation.