NASA Scientific and Technical Aerospace Reports
NASA Scientific and Technical Aerospace Reports
NASA Scientific and Technical Aerospace Reports
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[1] for solving difference equations with r<strong>and</strong>omly time-varying coefficients is thereby employed. Specifically, it is shown how<br />
higher order terms in the decomposition of the output correspond to the excess error caused by measurement noise in the<br />
coefficients. A simple example of an all-pole adaptive filter is included. It is shown that the misadjustment is largely dependent<br />
on the pole locations of the optimal filter.<br />
Author<br />
Adaptive Filters; IIR Filters; Coeffıcients; Position (Location); Difference Equations<br />
20060001612 Edinburgh Univ., UK<br />
Assessment of Finite Precision Limitations in LMS <strong>and</strong> BLMS Adaptive Algorithms<br />
P<strong>and</strong>a, G.; Cowan, C. F. N.; Grant, P. M.; IEEE International Conference on Acoustics, Speech, <strong>and</strong> Signal Processing<br />
(ICASSP ‘87); Volume 1; 1987, pp. 4.10.1 - 4.10.4; In English; See also 20060001583; Copyright; Avail.: Other Sources<br />
This paper presents a comparison of the effects of finite arithmetic on least mean squares (LMS) <strong>and</strong> block least mean<br />
squares (BLMS) adaptive algorithms. It is demonstrated, using both simulation <strong>and</strong> analytic results, that the BLMS algorithm<br />
consistently achieves improved converged noise performance compared to an LMS algorithm using the same word lengths.<br />
In the case dealt with here the improvement is equivalent to an excess 2-3 bits in word length.<br />
Author<br />
Algorithms; Mean Square Values; Words (Language)<br />
20060001615 Massachusetts Inst. of Tech., Cambridge, MA, USA<br />
Methods for Noise Cancellation Based on the EM Algorithm<br />
Feder, Meir; Oppenheim, Alan V.; Weinstein, Ehud; IEEE International Conference on Acoustics, Speech, <strong>and</strong> Signal<br />
Processing (ICASSP ‘87); Volume 1; 1987, pp. 6.11.1 - 6.11.4; In English; See also 20060001583<br />
Contract(s)/Grant(s): N00014-81-K-0742; N00014-85-K-0272; NSF ECS-84-07285; Copyright; Avail.: Other Sources<br />
Single microphone speech enhancement systems have typically shown limited performance, while multiple microphone<br />
systems based on a least-squares error criterion have shown encouraging results in some contexts. In this paper we formulate<br />
a new approach to multiple microphone speech enhancement. Specifically, we formulate a maximum likelihood (ML) problem<br />
for estimating the parameters needed for canceling the noise in a two microphone speech enhancement system. This ML<br />
problem is solved via the iterative EM (Estimate- Maximize) technique. The resulting algorithm shows encouraging results<br />
when applied to the speech enhancement problem.<br />
Author<br />
Maximum Likelihood Estimates; Microphones; Algorithms; Augmentation<br />
20060001616 Tsukuba Univ., Japan<br />
An Algorithm of Signal Approximation by Hybrid Spline<br />
Toraichi, Kazuo; Mori, Ryoichi; Sekita, Iwao; IEEE International Conference on Acoustics, Speech, <strong>and</strong> Signal Processing<br />
(ICASSP ‘87); Volume 1; 1987, pp. 7.1.1 - 7.1.4; In English; See also 20060001583; Copyright; Avail.: Other Sources<br />
Hybrid splines whose bases are constructed by multi-order B-splines are presented. B-splines of order 2 are induced for<br />
representing corner, <strong>and</strong> that of order 3 or 4 for representing curved parts. A presented hybrid spline of order 2 <strong>and</strong> 4 cannot<br />
be represented by conventional splines with multiple knots.<br />
Author<br />
Algorithms; Splines; Approximation; Signals<br />
20060001623 Tata Inst. of Fundamental Research, Bombay, India<br />
Estimation of Noise Variance from the Noisy AR Signal <strong>and</strong> Its Applications in Speech Enhancement<br />
Paliwal, K. K.; IEEE International Conference on Acoustics, Speech, <strong>and</strong> Signal Processing (ICASSP ‘87); Volume 1; 1987,<br />
pp. 8.3.1-8.3.4; In English; See also 20060001583; Copyright; Avail.: Other Sources<br />
In a number of applications involving the processing of noisy signals, it is desirable to know a priori the noise variance.<br />
We propose here a method of estimating the noise variance from the autoregressive (AR) signal corrupted by the additive<br />
white noise. This method first estimates the AR parameters from the high-order Yule-Walker equations <strong>and</strong> then uses these AR<br />
parameters to estimate the noise variance from the low-order Yule-Walker equations. The method is studied for a number of<br />
examples of noisy AR signals <strong>and</strong> its performance is found to be close to the Cramer-Rao lower bound for high signal-to-noise<br />
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