scholarly journals Speaker specific feature based clustering and its applications in language independent forensic speaker recognition

2020 ◽  
Vol 10 (4) ◽  
pp. 3508
Author(s):  
Satyanand Singh ◽  
Pragya Singh
Keyword(s):  
Speaker Recognition ◽  
Clustering Algorithms ◽  
Quantitative Measure ◽  
Fuzzy C Means ◽  
Reference Space ◽  
Feature Based ◽  
Ultimate Outcome ◽  
Clustering Approach ◽  
Recognition Efficiency

Forensic speaker recognition (FSR) is the process of determining whether the source of a questioned voice recording (trace) is a specific individual (suspected speaker). The role of the forensic expert is to testify by using, if possible, a quantitative measure of this value to the value of the voice evidence. Using this information as an aid in their judgments and decisions are up to the judge and/or the jury. Most existing methods measure inter-utterance similarities directly based on spectrum-based characteristics, the resulting clusters may not be well related to speaker’s, but rather to different acoustic classes. This research addresses this deficiency by projecting language-independent utterances into a reference space equipped to cover the standard voice features underlying the entire utterance set. The resulting projection vectors naturally represent the language-independent voice-like relationships among all the utterances and are therefore more robust against non-speaker interference. Then a clustering approach is proposed based on the peak approximation in order to maximize the similarities between language-independent utterances within all clusters. This method uses a K-medoid, Fuzzy C-means, Gustafson and Kessel and Gath-Geva algorithm to evaluate the cluster to which each utterance should be allocated, overcoming the disadvantage of traditional hierarchical clustering that the ultimate outcome can only hit the optimum recognition efficiency. The recognition efficiency of K-medoid, Fuzzy C-means, Gustafson and Kessel and Gath-Geva clustering algorithms are 95.2%, 97.3%, 98.5% and 99.7% and EER are 3.62%, 2.91 %, 2.82%, and 2.61% respectively. The EER improvement of the Gath-Geva technique based FSRsystem compared with Gustafson and Kessel and Fuzzy C-means is 8.04% and 11.49% respectively

Featureless Data Clustering

2010 ◽  
pp. 141-164 ◽  
Author(s):  
Wilson Wong
Keyword(s):  
Data Clustering ◽  
Dominant Role ◽  
Clustering Algorithms ◽  
Adaptive Clustering ◽  
Feature Based ◽  
Clustering Approach ◽  
Semantic Computation ◽  
The Many ◽  
Time Required ◽  
Existing Data

Feature-based semantic measurements have played a dominant role in conventional data clustering algorithms for many existing applications. However, the applicability of existing data clustering approaches to a wider range of applications is limited due to issues such as complexity involved in semantic computation, long pre-processing time required for feature preparation, and poor extensibility of semantic measurement due to non-incremental feature source. This chapter first summarises the many commonly used clustering algorithms and feature-based semantic measurements, and then highlights the shortcomings to make way for the proposal of an adaptive clustering approach based on featureless semantic measurements. The chapter concludes with experiments demonstrating the performance and wide applicability of the proposed clustering approach.

ENTROPY TOLERANT FUZZY C-MEANS IN MEDICAL IMAGES

2011 ◽  
Vol 04 (04) ◽  
pp. 447-462
Author(s):  
S. R. KANNAN ◽  
S. RAMATHILAGAM ◽  
R. DEVI ◽  
YUEH-MIN HUANG
Keyword(s):  
Fuzzy Clustering ◽  
Distance Measure ◽  
Clustering Algorithms ◽  
Magnetic Resonance Images ◽  
Significant Feature ◽  
Fuzzy C Means ◽  
Robust Segmentation ◽  
Contrast Enhanced ◽  
Segmentation Algorithms ◽  
Clustering Approach

Segmenting the Dynamic Contrast-Enhanced Breast Magnetic Resonance Images (DCE-BMRI) is an extremely important task to diagnose the disease because it has the highest specificity when acquired with high temporal and spatial resolution and is also corrupted by heavy noise, outliers, and other imaging artifacts. In this paper, we intend to develop efficient robust segmentation algorithms based on fuzzy clustering approach for segmenting the DCE-BMRs. Our proposed segmentation algorithms have been amalgamated with effective kernel-induced distance measure on standard fuzzy c-means algorithm along with the spatial neighborhood information, entropy term, and tolerance vector into a fuzzy clustering structure for segmenting the DCE-BMRI. The significant feature of our proposed algorithms is its capability to find the optimal membership grades and obtain effective cluster centers automatically by minimizing the proposed robust objective functions. Also, this article demonstrates the superiority of the proposed algorithms for segmenting DCE-BMRI in comparison with other recent kernel-based fuzzy c-means techniques. Finally the clustering accuracies of the proposed algorithms are validated by using silhouette method in comparison with existed fuzzy clustering algorithms.

scholarly journals Halloysite nanotubes filled with salicylic acid and sodium diclofenac: effects of vacuum pumping on loading and release properties

2021 ◽  
Author(s):  
Lorenzo Lisuzzo ◽  
Giuseppe Cavallaro ◽  
Stefana Milioto ◽  
Giuseppe Lazzara
Keyword(s):  
Salicylic Acid ◽  
Release Kinetics ◽  
Halloysite Nanotubes ◽  
Drug Molecules ◽  
Sodium Diclofenac ◽  
Tunable Release ◽  
Feature Based ◽  
Kinetics Of ◽  
Filling Mechanism

AbstractIn this work, we investigated the effects of the vacuum pumping on both the loading efficiencies and the release kinetics of halloysite nanotubes filled with drug molecules dissolved in ethanol. As model drugs, salicylic acid and sodium diclofenac were selected. For comparison, the loading of the drug molecules was conducted on platy kaolinite to explore the key role of the hollow tubular morphology on the filling mechanism of halloysite. The effects of the pressure conditions used in the loading protocol were interpreted and discussed on the basis of the thermodynamic results provided by Knudsen thermogravimetry, which demonstrated the ethanol confinement inside the halloysite cavity. Several techniques (TEM, FTIR spectroscopy, DLS and $$\zeta$$ ζ -potential experiments) were employed to characterize the drug filled nanoclays. Besides, release kinetics of the drugs were studied and interpreted according to the loading mechanism. This work represents a further step for the development of nanotubular carriers with tunable release feature based on the loading protocol and drug localization into the carrier. Graphic abstract The filling efficiency of halloysite nanotubes is enhanced by the reduction of the pressure conditions used in the loading protocol.

A Hard C-Means Clustering Algorithm Incorporating Membership KL Divergence and Local Data Information for Noisy Image Segmentation

2017 ◽  
Vol 32 (04) ◽  
pp. 1850012 ◽  
Author(s):  
R. R. Gharieb ◽  
G. Gendy ◽  
H. Selim
Keyword(s):  
Image Segmentation ◽  
Membership Function ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cluster Center ◽  
Local Data ◽  
Cluster Membership ◽  
Kl Divergence ◽  
Clustering Approach ◽  
Center Distance

In this paper, the standard hard C-means (HCM) clustering approach to image segmentation is modified by incorporating weighted membership Kullback–Leibler (KL) divergence and local data information into the HCM objective function. The membership KL divergence, used for fuzzification, measures the proximity between each cluster membership function of a pixel and the locally-smoothed value of the membership in the pixel vicinity. The fuzzification weight is a function of the pixel to cluster-centers distances. The used pixel to a cluster-center distance is composed of the original pixel data distance plus a fraction of the distance generated from the locally-smoothed pixel data. It is shown that the obtained membership function of a pixel is proportional to the locally-smoothed membership function of this pixel multiplied by an exponentially distributed function of the minus pixel distance relative to the minimum distance provided by the nearest cluster-center to the pixel. Therefore, since incorporating the locally-smoothed membership and data information in addition to the relative distance, which is more tolerant to additive noise than the absolute distance, the proposed algorithm has a threefold noise-handling process. The presented algorithm, named local data and membership KL divergence based fuzzy C-means (LDMKLFCM), is tested by synthetic and real-world noisy images and its results are compared with those of several FCM-based clustering algorithms.

Construct Knowledge Structure of Linear Algebra

2011 ◽  
Vol 211-212 ◽  
pp. 793-797
Author(s):  
Chin Chun Chen ◽  
Yuan Horng Lin ◽  
Jeng Ming Yih ◽  
Sue Fen Huang
Keyword(s):  
Knowledge Management ◽  
Linear Algebra ◽  
Fuzzy Clustering ◽  
Mahalanobis Distance ◽  
Clustering Algorithms ◽  
Knowledge Structure ◽  
Interpretive Structural Modeling ◽  
Cognitive Characteristics ◽  
Fuzzy C Means ◽  
Fuzzy C Means Algorithm

Apply interpretive structural modeling to construct knowledge structure of linear algebra. New fuzzy clustering algorithms improved fuzzy c-means algorithm based on Mahalanobis distance has better performance than fuzzy c-means algorithm. Each cluster of data can easily describe features of knowledge structures individually. The results show that there are six clusters and each cluster has its own cognitive characteristics. The methodology can improve knowledge management in classroom more feasible.

Management of Abstract Algebra Concepts Based on Knowledge Structure

2013 ◽  
Vol 284-287 ◽  
pp. 3537-3542
Author(s):  
Chin Chun Chen ◽  
Yuan Horng Lin ◽  
Jeng Ming Yih
Keyword(s):  
Mahalanobis Distance ◽  
Clustering Algorithm ◽  
Fuzzy Theory ◽  
Clustering Algorithms ◽  
Knowledge Structures ◽  
Interpretive Structural Modeling ◽  
Fuzzy C Means ◽  
Integrated Method ◽  
Combined Algorithm ◽  
Fuzzy C Means Algorithm

Knowledge Management of Mathematics Concepts was essential in educational environment. The purpose of this study is to provide an integrated method of fuzzy theory basis for individualized concept structure analysis. This method integrates Fuzzy Logic Model of Perception (FLMP) and Interpretive Structural Modeling (ISM). The combined algorithm could analyze individualized concepts structure based on the comparisons with concept structure of expert. Fuzzy clustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. A Fuzzy C-Means algorithm based on Mahalanobis distance (FCM-M) was proposed to improve those limitations of GG and GK algorithms, but it is not stable enough when some of its covariance matrices are not equal. A new improved Fuzzy C-Means algorithm based on a Normalized Mahalanobis distance (FCM-NM) is proposed. Use the best performance of clustering Algorithm FCM-NM in data analysis and interpretation. Each cluster of data can easily describe features of knowledge structures. Manage the knowledge structures of Mathematics Concepts to construct the model of features in the pattern recognition completely. This procedure will also useful for cognition diagnosis. To sum up, this integrated algorithm could improve the assessment methodology of cognition diagnosis and manage the knowledge structures of Mathematics Concepts easily.

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