Seong-Joon Baek

Modified K-means algorithm for vector quantizer design

The K-means algorithm is widely used in vector quantizer (VQ) design and clustering analysis. In VQ context, this algorithm iteratively updates an initial codebook and converges to a locally optimal codebook in certain conditions. It iteratively satisfies each of the two necessary conditions for an optimal quantizer; the nearest neighbor condition for the partition and centroid condition for the codevectors. In this letter, we propose a new algorithm for both vector quantizer design and clustering analysis as an alternative to the conventional K-means algorithm. The algorithm is almost the same as the K-means algorithm except for a modification at codebook updating step. It does not satisfy the centroid condition iteratively, but asymptotically satisfies it as the number of iterations increases. Experimental results show that the algorithm converges to a better locally optimal codebook with an accelerated convergence speed.

A fast encoding algorithm for vector quantization

In this letter, we present a fast encoding algorithm for vector quantization that uses two characteristics of a vector, mean, and variance. Although a similar method using these features was already proposed, it handles these features separately, On the other hand, the proposed algorithm utilizes these features simultaneously to save computation time all the more. Since the proposed algorithm rejects those codewords that are impossible to be the nearest codeword, it produces the same output as the conventional full search algorithm. The simulation results confirm the effectiveness of the proposed algorithm.

Time-varying signal frequency estimation by VFF Kalman filtering

Abstract In this paper, a new algorithm is proposed for estimating a time-varying signal frequency. The proposed method introduces a variable forgetting factor (VFF) into Kalman filter so that this approach does not require any pre-determined forgetting factor. The proposed method is tested on two nonstationary signals. Results show that the proposed algorithm offers a more accurate estimation of signal frequency and faster convergence speed than conventional Kalman filtering algorithm.

A fast vector quantization encoding algorithm using multiple projection axes

Computation of nearest neighbor generally requires a large number of expensive distance calculations. In this paper, we present an algorithm which uses multiple projection axes to accelerate the encoding process of VQ by eliminating the necessity of calculating many distances. Since the proposed algorithm rejects those codewords that are impossible to be the nearest codeword, it produces the same output as a conventional full search algorithm. The simulation results confirm the effectiveness of the proposed algorithm.

Application of MAP and MLP Classifier on Raman Spectral Data for Classification of Liver Disease

In this paper, we evaluated the performance of the automatic classifier applied for the discrimination of acute alcoholic liver injury and chronic liver fibrosis. The classifier uses the discriminant peaks of the preprocessed Raman spectrum as a feature set. In preprocessing step, we subtract baseline and apply Savitzky-Golay smoothing filter which is known to be useful at preserving peaks. After identifying discriminant peaks from the spectra, we carried out the classification experiments using MAP and neural networks. According to the experimental results, the classifier shows the promising results to diagnosis alcoholic liver injury and chronic liver fibrosis. Classification results over 80% means that the peaks used as a feature set is useful for diagnosing liver disease.

Mixture IMM for speech enhancement under nonstationary noise

A mixture interacting multiple model (MIMM) algorithm is proposed to enhance speech contaminated by additive nonstationary noise. In this approach, a mixture hidden filter model (HFM) is used for clean speech modeling and a single hidden filter is used for noise process modeling. The MIMM algorithm gives better enhancement results than the IMM algorithm. The results show that the proposed method offers performance gain compared to the previous results in with slightly increased complexity.