In this paper we describe a noise reduction preprocessing algorithm for the adaptive multirate (AMR) speech codec of the GSM system. The algorithm is based on spectral weighting and explicitly takes into account the properties of the human auditory system. The weighting rule results in the smallest possible speech distortion under the constraint that the background noise should exhibit no audib...

Original and cumulative prospect theory differ in the composition rule used to combine the probability weighting function and the value function. We test these composition rules by performing a novel test. We apply estimates of prospect theory’s weighting and value function obtained from two-outcome cash equivalents, a domain where original and cumulative prospect theory coincide, to three-outc...

In this paper an unconventional, simple BPSK demodulator is proposed. Complexity of the conventional BPSK demodulator is reduced by utilizing a l-bit Analog-to-Digital (A/D) converter front-end. The optimal detection rule is derived for BPSK signals going through the l-bit A/D front-end. The key element of the optimal demodulation process is a pair of weighting factors that act as phase referen...

When does a gradual learning rule translate into gradual learning performance? This paper studies a gradient-ascent Maximum Entropy phonotactic learner, as applied to twoalternative forced-choice performance expressed as log-odds. The main result is that slow initial performance cannot accelerate later if the initial weights are near zero, but can if they are not. Stated another way, abruptness...

A generating equation for the mixing rules of interatomic potential energy parameters is proposed. It is demonstrated that this equation can, indeed, reproduce many popular mixing rules. A weighting matrix is used with the generating equation. This weighting matrix approach is superior to the present status of mixing rule development. A systematic framework is given for devising new mixing rule...

Fuzzy rule based classification systems is one of the most popular in pattern classification problems. The rules in the fuzzy models can be weighted to show the importance of generated rules where all attributes in the antecedent part of the rules have been usually weighted equally. Whereas the contributed attributes in a fuzzy model may have different influences on the decision making, a new m...

Feature selection and feature weighting are useful techniques for improving the classification accuracy of K-nearest-neighbor (K-NN) rule. The term feature selection refers to algorithms that select the best subset of the input feature set. In feature weighting, each feature is multiplied by a weight value proportional to the ability of the feature to distinguish pattern classes. In this paper,...

Ranking of relevant objects plays an important role in various applications especially in multimedia database systems and information retrieval systems. In contrast to traditional database systems, multimedia database systems deal with similarity queries returning a list of objects ranked by the objects’ overall score. The overall score for objects in the database is calculated using a scoring ...

Rule weights often have been used to improve the classification accuracy without changing the position of antecedent fuzzy sets. Recently, fuzzy versions of confidence and support merits from the field of data mining have been widely used for rules weighting in fuzzy rule based classifiers. This paper proposes an evolutionary approach for learning rule weights and uses more flexible equations, ...

This paper explores a relationship between parameters for the context tree weighting and weights for a general model weighting technique. In particular, an algorithm is proposed that approximately computes the parameters from the weights, and a condition under which no error for the approximation occurs is derived. key words: model weighting technique, tree source, context tree weighting.