Design of codebook using Centroid Neural Network with state dependence measure

A classification scheme for content-based audio signal retrieval is proposed in this paper. The proposed scheme uses the Centroid Neural Networks (CNN) with a Divergence Measure called Divergence-based Centroid Neural Network (DCNN) to perform clustering of Gaussian Probability Density Function (GPDF) data. In comparison with other conventional algorithms, the DCNN designed for probability data has the robustness advantages of utilizing a audio data representation method in which each audio data is represented by a Gaussian distribution feature vector. Experiments and results for several audio data sets have shown that the DCNN-based classification algorithm has accuracy improvements over models employing the conventional k-means and Self Organizing Map (SOM) algorithms.

An unsupervised competitive learning algorithm based on the classical -means clustering algorithm is proposed. The proposed learning algorithm called the centroid neural network (CNN) estimates centroids of the related cluster groups in training date. This paper also explains algorithmic relationships among the CNN and some of the conventional unsupervised competitive learning algorithms including Kohonen's self-organizing map (SOM) and Kosko's differential competitive learning (DCL) algorithm. The CNN algorithm requires neither a predetermined schedule for learning coefficient nor a total number of iterations for clustering. The simulation results on clustering problems and image compression problems show that CNN converges much faster than conventional algorithms with compatible clustering quality while other algorithms may give unstable results depending on the initial values of the learning coefficient and the total number of iterations.

Computational cancer phylogenetics can play an important role in delineating possible tumor progression pathways and identifying molecular subtypes and mechanisms of action. We previously developed a pipeline for constructing tumor phylogenies from recurring cell types computationally inferred from whole genome copy number data. The accuracy and detail of these tumor phylogenies, however, depends on the identification of accurate and high-resolution molecular markers of progression, i.e., reproducible regions of copy number variation that can be used to robustly differentiate different subtypes and stages of progression. Here we present a new method for the problem using hidden Markov models (HMMs) to derive robust, high resolution progression markers from sets of tumor samples. We demonstrate our method on a publicly available array comparative genome hybridization (aCGH) dataset (NCBI GEO GSE16672, Navin et al., 2010) from sectioned primary ductal breast tumors. Our method uses an HMM, a class of probabilistic models, to classify sets of aCGH data into a partitioning of samples into normal (diploid) or amplified at each copy number probe. It differs from other similar HMM methods primarily in seeking a parsimonious set of combinations of amplification states able to explain all aCGH profiles simultaneously in order to identify robust markers of progression across samples. The model learns frequencies with which different combinations of amplifications are observed across the samples by modeling individual probes as Gaussian random variables with either normal or tetraploid means, with data more consistent with tetraploid being classified as amplified and those more consistent with diploid classified as normal. To handle a combinatorial explosion in combinations of amplification states with increasing numbers of samples, the method introduces a Gibbs sampling algorithm to learn a parsimonious model of the most frequently occurring combinations of amplification states. We applied our methods to a previously constructed set of inferred cell types derived from the Navin et al. data (Tolliver et al., 2010) and to a comparison set of 9 random samples from the raw aCGH data. We validated our model relative to manual labeling of amplicons on the same data. In both experiments, the HMM method was able to pick up significantly larger numbers of robustly amplified segments per chromosome than did prior methods or manual analysis. The resulting segments can be directly fed into downstream analysis routines for phylogeny inference or other predictions. In future work, the HMM method may be improved by fine-tuning the underlying model for copy number variation. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3964. doi:1538-7445.AM2012-3964

A noise deduction method for image data by using centroid neural network (CNN) is proposed in this paper. The spatial constraints are applied to neural network(CNN) algorithm to reduce noise in image data. The proposed algorithm incorporates a novel approach of using the weights of attributes to evaluate the roles of the latter. Experiments and results on a set of synthetic data show that the proposed algorithm provides superior performance over other algorithms such as fuzzy C-means(FCM)and fuzzy C-means with spatial constraints(FCM-S).

A Centroid Neural Network with spatial constraints (CNN-S) is proposed in this paper. The spatial constraints are applied to the Centroid Neural Network(CNN) algorithm to reduce noise in Magnetic Resonance(MR) images. MR image segmentation is performed to illustrate the application of the proposed algorithm. The proposed algorithm incorporates a novel approach of using the weights of attributes to evaluate the roles of the latter. Experiments and results on MR images from Internet Brain Segmentation Repository(IBSR) show that the proposed algorithm provides a superior performance over other algorithms such as Fuzzy C-Means(FCM)and Fuzzy C-Means with spatial constraints(FCM-S).

Arabic language can be used by native and non- native speakers; due to Arabic is the language of the holy book of Muslims. In this paper, Arabic phoneme recognition system is proposed based on Malay speakers. This system consists of three main stages. The first stage is noise reduction and it aims to enhance the phoneme signals by excluding the unvoiced signals and keep only the voiced signal. Wiener filter is adapted to accomplish this task. The second stage is based on Mel-Frequency Cepstral Coefficients method to extract a vector of features to represent each phoneme signal. Eventually, pattern recognition neural network is designed as recognizer. The proposed system produces sufficient outcomes with 20 hidden neurons. Keywords—Arabic; Malay; pattern recognition; Wiener; Mel- Frequency Cepstral Coefficients I. INTRODUCTION It is well known the wide range of Artificial Neural Networks (ANN) application in speech recognition, financial, telecommunications, electronics and medical. As for speech recognition system for Arabic language, (1) & (2) have addressed the implementation of recognition system based on Arabic native speakers. In general, neural networks (NNs) for phonemes recognition are divided into several categories for instance Very Large Scale Integration (VLSI) NN that can be divided into digital and analogue with digital NNs being more compatible with feed-forward neural networks while analogue NNs are found to be more successful with recurrent NNs (3). From Automatic Speech System (ASR) point of view, NNs for recognition purpose can be divided into two main category namely conventional neural networks such as Multilayer Perceptron (MLP) and Radial Basis Function (RBF) and secondly Recurrent Neural Networks (RNN). The first category of NNs was implemented as pattern classifiers and proven capable for recognition purpose but not at par as compared to Hidden Markov Model (HMM) performance (2). At present, HMM has been proven to be the most successful approach in ASR research area until recently the possibility to hybrid both HMM and ANN (4) & (5). In general, speech recognition systems consist of three stages specifically noise reduction, feature extraction and classification. The aim of noise reduction is to reduce the level of noise in the speech signal and make it more compatible for the ASR. As for feature extraction, this process attempted to extract the most relevant features related to the phoneme signal. The extracted features acted as input features of the NN during training and testing. Finally, the purpose of classification process is to arrange the most similar and related features in categories based on the training phase. Basically, networks training database are being observed and updated during training session of the networks, involving the weights and biases arguments. Networks with lower errors can be considered as better networks (6). Hence, this paper deems to explore further the ability of NN for Arabic phonemes recognition spoken by Malay individuals. This paper is arranged as following; related work is discussed in section II followed by section III that discussed method to be implemented based on Wiener filter, Mel-Frequency Cepstral Coefficients and pattern recognition neural network. Section IV elaborated experimental analysis and results and followed by Conclusion section.

Speech to Text was one of speech recognition applications which speech signal was processed, recognized and converted into a textual representation. Hidden Markov Model (HMM) was the widely used method in speech recognition. However, the level of accuracy using HMM was strongly influenced by the optimalization of extraction process and modellling methods. Hence in this research, the use of genetic algorithm (GA) method to optimize the Ergodic HMM was tested. In Hybrid HMM-GA, GA was used to optimize the Baum-welch method in the training process. It was useful to improve the accuracy of the recognition result which is produced by the HMM parameters that generate the low accuracy when the HMM are tested. Based on the research, the percentage increases the level of accuracy of 20% to 41%. Proved that the combination of GA in HMM method can gives more optimal results when compared with the HMM system that not combine with any method.

Prediction of 24-hour-average PM2.5 concentrations using a hidden Markov model with different emission distributions in Northern California

This paper presents a minimum generation error (MGE) training method for hidden Markov model (HMM) based prediction of articulatory movements when both text and audio inputs are given. In this method, MGE criterion is adopted to replace the maximum likelihood (ML) criterion to estimate model parameters for the unified acoustic-articulatory HMMs. Different from the MGE training for HMM-based acoustic speech synthesis, the generation error used here is defined as the distance between the generated and natural articulatory features. Experimental results show that our proposed method can improve the accuracy of articulatory movement prediction significantly. The average root mean square (RMS) error reduces from 1.002 mm to 0.913 mm on the test set.

This paper introduces a non-temporal multiple silhouettes in Hidden Markov Model (HMM) for offering view independent human posture recognition. The multiple silhouettes are used to reduce the ambiguity problem of posture recognition. A simple feature extraction of the 2D shape contour based histogram is used for image encoding and K-Means algorithm is applied for clustering and code-wording of eight simple postures from multiple views. Therefore, 3D volume reconstruction is not required, in return helps to reduce the complexity of modeling and computational power of feature extraction. HMM is trained to obtain view independent recognition model using multiple silhouettes. A combination of non-temporal multiple silhouettes, code-wording and HMM methods in this proposed approach make it possible to recognize human posture in view independent. The experimental results demonstrate the effectiveness of non-temporal multiple silhouettes in HMM for recognizing posture.

It is very important to enhance the recognition accuracy of the Arabic spoken letters. The accuracy of recognition system is affected by the feature extraction and the used classifier. An effective and robust method is proposed to evaluate speech feature to improve the performance the recognition accuracy. This work introduces applying the mel frequency cepstral coefficient (MFCC) to extract the speech features. Hidden Markov model and neural network are used as classifier tools. The objective of the proposed system is to enhance the performance by introducing three systems which are proposed to recognize the spoken Arabic letters. The first is based on neural networks. The second is based on hidden Markov model. While third system is based on combination between neural networks and hidden Markov models. The accuracy of neural network is found to be 42% with MFCC for 84 spoken letters. The hidden Markov models are statistical based approach. Its performance is found to be 98.5%. But for combination system based on neural network and hidden Markov models, the accuracy of 99.25% is obtained.

The central nervous system (CNS) plays an important role in regulation of human gait. Parkinson's disease (PD) is a common neurodegenerative disease that may cause neurophysiologic change in the CNS and as a result change the gait cycle duration (stride interval). This article used the Hidden Markov Model (HMM) with Gaussian Mixtures to separate the patients with PD from healthy subjects. The results showed that the performance of the HMM classifier in classifying the gait data corresponding to 16 healthy and 15 PD subjects is comparable to the results obtained from the least squares support vector machine (LS-SVM) classifier. In this study, the leave-one-out cross-validation method was used to evaluate the performance of each classifier. The HMM method could effectively separate the gait data in terms of stride interval obtained from healthy subjects and PD patients with an accuracy rate of 90.3 % . All in all, the results showed that the proposed method can be used for distinguishing PD patients from healthy subjects based on the gait data classification.

A method is presented for using hidden Markov models (HMMs) for the analysis of force, torque, and position signals from sensors in manufacturing machines. The HMM can detect the transitions between contact states and compute a measure of the task quality using a model of the task developed by the manufacturing engineer and optimized on training data. The HMM method has been evaluated in extensive experimentation with teleoperation and the results suggest even higher effectiveness in automation and manufacturing applications. >

Face recognition based on multi-class mapping of Fisher scores

In this paper, we propose a new objective evaluation method for hidden Markov model (HMM)-based speech synthesis using Kullback-Leibler divergence (KLD). The KLD is used to measure the difference between the probability density functions (PDFs) of the acoustic feature vectors extracted from natural training and synthetic speech data. For the evaluation, Gaussian mixture model (GMM) is used to model the distribution of acoustic feature vectors, including the fundamental frequency (F0). Continuous F0, obtained with linear interpolation, is used in the evaluation. In essence, the KLD is the expectation of the logarithmic difference between the likelihoods calculated on training and synthetic speech. This likelihood difference is appropriate to characterize the quality of a HMM-based speech synthesis system in generating synthetic speech using a maximum likelihood criterion. The objective evaluation is tested with 3 different HMM-based speech synthesis systems which use multi-space distribution (MSD) to model discontinuous F0. These systems are trained on a common speech corpus in French. We propose an index to evaluate HMM-based speech synthesis system which takes into account the relative variation of the KLDs on test sets of synthetic and natural speech. This index correlates inversely with the result of the MOS (mean opinion score) perceptual test.