NASA Scientific and Technical Aerospace Reports

This paper introduces an adaptive filtering method that rejects interfering speech from nonpreferred directions. Phase
delays in interfering signals received by three receivers are used to obtain a low signal-to-noise ratio (SNR) reference input
for an adaptive filter. Since adaptive filter output SNR is inversely proportional to its reference input SNR, this method works
very well theoretically. To minimize the excess steady-state error in conventional schemes, a master-slave structure is
introduced. Simulations with digitized speech data, tested both by comparing waveforms and by listening, showed significant
improvements. Due to the computational simplicity of the method, real-time realization is feasible.
Author
Noise Reduction; Signal to Noise Ratios; Adaptive Filters; Real Time Operation; Waveforms
20060001708 Texas Univ., Austin, TX, USA
Texture Segmentation Using a Class of Narrowband Filters
Clark, marianna; Bovik, Alan C.; Geisler, Wilson S.; IEEE International Conference on Acoustics, Speech, and Signal
Processing (ICASSP ‘87); Volume 1; 1987, pp. 14.6.1 - 14.5.4; In English; See also 20060001583; Copyright; Avail.:
Other Sources
A class of 2D filters is proposed for segmenting visible images into regions of uniform texture. The filters used, known
as Gabor filters, are optimal in several senses: they have tunable orientation bandwidths, they can be defined to operate over
a range of spatial frequency channels, and they obey the uncertainty principle in two dimensions. The filters are interpreted
as transforming the image into a modulated narrowband signal whose envelope coincides with the textured region to which
the filter is tuned. Moreover, the receptive fields of neurons in the visual cortex are known to have shapes that approximate
2D Gabor filters, whose purpose has been uncertain. We suggest that they may play an important role in texture
segmentation/surface perception. The technique is demonstrated using a variety of natural and synthetic textures.
Author
Narrowband; Textures; Segments; Gabor Filters
20060001723 Massachusetts Inst. of Tech., Lexington, MA, USA
Efficient CFAR Detection of Line Segments in a 2-D Image
Chu, Peter L.; IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ‘87); Volume 1; 1987,
pp. 587-590; In English; See also 20060001583; Copyright; Avail.: Other Sources
A computationally efficient algorithm is proposed for detecting line segments in an image of additive, i.i.d. (independent,
identically distributed) Gaussian noise. Meteors, satellites, or other moving objects may be optically detected using the
algorithm. A CFAR (Constant False Alarm Rate) characteristic is designed into the algorithm to give equal probabilities of
false alarm for all streak lengths. Compared to the 2-D optimum matched filter approach, the algorithm loses 2 dB in
signal-to-noise ratio, but requires hundreds of times less computation.
Author
Detection; False Alarms; Mathematical Models; Images; Algorithms
20060001726 Bell Telephone Labs., Inc., Murray Hill, NJ, USA
Speech Parameter Estimation Using A Vocal Tract/Cord Model
Schroeter, J.; Larar, J. N.; Sondhi, M. M.; IEEE International Conference on Acoustics, Speech, and Signal Processing
(ICASSP ‘87); Volume 1; 1987, pp. 8.6.1 - 8.6.4; In English; See also 20060001583; Copyright; Avail.: Other Sources
This paper proposes the use of a vocal cord and tract model for speech coding at bit rates below 4.8 kb/s. For this. a key
requirement is the ability to derive model parameters from an input speech signal. Our approach to this problem employs an
acoustic analysis front-end, a linked codebook of vocal-tract configurations and related acoustic characteristics, and an
optimizing articulatory synthesizer. While the acoustic front-end is relatively straight-forward involving LPC. pitch, and
voicing analyses, the codebook design and usage, as well as the specific method for optimizing the model parameters are new.
The codebook is intended to provide good starting values for an iterative optimization, thus alleviating the problem of locking
on to a locally optimum solution. In a first stage of optimization, the best vocal tract configuration found in the codebook is
refined by varying only the vocal tract parameters. Then. in a second stage of optimization, the best match is found between
the global waveform of the model and the inverse filtered input speech.
Author
Voice Data Processing; Speech Recognition; Signal Analyzers; Parameter Identification; Speech
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