Experimentation Using Short-Term Spectral Features for Secure Mobile Internet Voting Authentication

We propose a secure mobile Internet voting architecture based on the Sensus reference architecture and report the experiments carried out using short-term spectral features for realizing the voice biometric based authentication module of the architecture being proposed. The short-term spectral features investigated are Mel-Frequency Cepstral Coefficients (MFCCs), Mel-Frequency Discrete Wavelet Coefficients (MFDWC), Linear Predictive Cepstral Coefficients (LPCC), and Spectral Histogram of Oriented Gradients (SHOGs). The MFCC, MFDWC, and LPCC usually have higher dimensions that oftentimes lead to high computational complexity of the pattern matching algorithms in automatic speaker recognition systems. In this study, higher dimensions of each of the short-term features were reduced to an 81-element feature vector per Speaker using Histogram of Oriented Gradients (HOG) algorithm while neural network ensemble was utilized as the pattern matching algorithm. Out of the four short-term spectral features investigated, the LPCC-HOG gave the best statistical results withRstatistic of 0.9127 and mean square error of 0.0407. These compact LPCC-HOG features are highly promising for implementing the authentication module of the secure mobile Internet voting architecture we are proposing in this paper.

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System architecture for secure mobile internet voting

10.51415/10321/1393 ◽

2015 ◽

Author(s):

◽

Surendra Thakur

Keyword(s):

Pattern Matching ◽

System Architecture ◽

Higher Dimensions ◽

Mobile Internet ◽

Security Requirements ◽

Spectral Features ◽

Histogram Of Oriented Gradients ◽

Internet Voting ◽

Voting System ◽

Cepstral Coefficients

This thesis focuses on the development of an enhanced innovative secure mobile Internet voting system architecture that offers desirable security requirements to theoretically mitigate some of the intrinsic administrative and logistical challenges of voting, inter alia lack of mobility support for voters, voter inconvenience, election misconduct, and possible voter coercion often associated with the conventional poll-site voting system. Systems in existence have tended to revolve around the need to provide ubiquitous voting, but lack adequate control mechanism to address, in particular, the important security requirement of controlling possible coercion in ubiquitous voting. The research work reported in this thesis improves upon a well-developed Sensus reference architecture. It does so by leveraging the auto-coupling capability of near field communication, as well as the intrinsic merits of global positioning system, voice biometric authentication, and computational intelligence techniques. The leveraging of the combination of these features provides a theoretical mitigation of some of the security challenges inherent in electoral systems previously alluded to. This leveraging also offers a more pragmatic approach to ensuring high level, secure, mobile Internet voting such as voter authentication. Experiments were performed using spectral features for realising the voice biometric based authentication of the system architecture developed. The spectral features investigated include Mel-frequency Cepstral Coefficients (MFCC), Mel-frequency Discrete Wavelet Coefficients (MFDWC), Linear Predictive Cepstral Coefficients (LPCC), and Spectral Histogram of Oriented Gradients (SHOG). The MFCC, MFDWC and LPCC usually have higher dimensions that oftentimes lead to high computational complexity of the pattern matching algorithms in automatic speaker authentication systems. In this study, higher dimensions of each of the features were reduced per speaker using Histogram of Oriented Gradients (HOG) algorithm, while neural network ensemble was utilised as the pattern-matching algorithm. Out of the four spectral features investigated, the LPCC-HOG gave the best statistical results with an R statistic of 0.9257 and Mean Square Error of 0.0361. These compact LPCC-HOG features are highly promising for implementing the authentication module of the secure mobile Internet voting system architecture reported in this thesis.

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A Two-Level Speaker Identification System via Fusion of Heterogeneous Classifiers and Complementary Feature Cooperation

Sensors ◽

10.3390/s21155097 ◽

2021 ◽

Vol 21 (15) ◽

pp. 5097

Author(s):

Mohammad Al-Qaderi ◽

Elfituri Lahamer ◽

Ahmad Rad

Keyword(s):

Speaker Identification ◽

Signal To Noise Ratio ◽

Support Vector ◽

Spectral Features ◽

Prosodic Features ◽

Signal To Noise ◽

Short Term ◽

Classifier System ◽

Cepstral Coefficients ◽

Noise Ratio

We present a new architecture to address the challenges of speaker identification that arise in interaction of humans with social robots. Though deep learning systems have led to impressive performance in many speech applications, limited speech data at training stage and short utterances with background noise at test stage present challenges and are still open problems as no optimum solution has been reported to date. The proposed design employs a generative model namely the Gaussian mixture model (GMM) and a discriminative model—support vector machine (SVM) classifiers as well as prosodic features and short-term spectral features to concurrently classify a speaker’s gender and his/her identity. The proposed architecture works in a semi-sequential manner consisting of two stages: the first classifier exploits the prosodic features to determine the speaker’s gender which in turn is used with the short-term spectral features as inputs to the second classifier system in order to identify the speaker. The second classifier system employs two types of short-term spectral features; namely mel-frequency cepstral coefficients (MFCC) and gammatone frequency cepstral coefficients (GFCC) as well as gender information as inputs to two different classifiers (GMM and GMM supervector-based SVM) which in total leads to construction of four classifiers. The outputs from the second stage classifiers; namely GMM-MFCC maximum likelihood classifier (MLC), GMM-GFCC MLC, GMM-MFCC supervector SVM, and GMM-GFCC supervector SVM are fused at score level by the weighted Borda count approach. The weight factors are computed on the fly via Mamdani fuzzy inference system that its inputs are the signal to noise ratio and the length of utterance. Experimental evaluations suggest that the proposed architecture and the fusion framework are promising and can improve the recognition performance of the system in challenging environments where the signal-to-noise ratio is low, and the length of utterance is short; such scenarios often arise in social robot interactions with humans.

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Mapping the Sharing Economy in China

Sustainability ◽

10.3390/su12166333 ◽

2020 ◽

Vol 12 (16) ◽

pp. 6333

Author(s):

Chan Liu ◽

Raymond K. H. Chan ◽

Maofu Wang ◽

Zhe Yang

Keyword(s):

Global Economy ◽

Rapid Development ◽

Sharing Economy ◽

Essential Elements ◽

Mobile Internet ◽

Internet Technology ◽

Short Term ◽

Bona Fide ◽

The Government ◽

Idle Capacity

Harnessing the rapid development of mobile internet technology, the sharing economy has experienced unprecedented growth in the global economy, especially in China. Likely due to its increasing popularity, more and more businesses have adopted this label in China. There is a concern as to the essential meaning of the sharing economy. As it is difficult to have a universally accepted definition, we aim to map the sharing economy and demystify the use of it in China in this paper. We propose seven organizing essential elements of the sharing economy: access use rights instead of ownership, idle capacity, short term, peer-to-peer, Internet platforms mediated, for monetary profit, and shared value orientation. By satisfying all or only parts of these elements, we propose one typology of sharing economy, and to differentiate bona fide sharing economy from quasi- and pseudo-sharing economy. Finally, there are still many problems that need to be solved urgently in the real sharing economy from the perspective of the government, companies and individuals.

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Subspace analysis of spectral features for speaker recognition

2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) ◽

10.1109/fskd.2014.6980814 ◽

2014 ◽

Cited By ~ 1

Author(s):

Ling Chen ◽

Hong Man ◽

Huading Jia ◽

Zhiyi Wang ◽

Lei Wang ◽

...

Keyword(s):

Speaker Recognition ◽

Spectral Features ◽

Subspace Analysis

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Discrete Wavelet Transform for automatic speaker recognition

2010 3rd International Congress on Image and Signal Processing ◽

10.1109/cisp.2010.5646691 ◽

2010 ◽

Cited By ~ 3

Author(s):

Pavel Kral

Keyword(s):

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Speaker Recognition ◽

Discrete Wavelet ◽

Automatic Speaker Recognition

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Short-term wind speed prediction based on data preprocessing with discrete wavelet transform-mutual information and neural network

10.1109/iciea51954.2021.9516271 ◽

2021 ◽

Author(s):

Ran Zhao ◽

Jian Yang ◽

Dongran Song ◽

Li Wang ◽

Junbo Liu ◽

...

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Wind Speed ◽

Mutual Information ◽

Discrete Wavelet Transform ◽

Data Preprocessing ◽

Discrete Wavelet ◽

Short Term ◽

Wind Speed Prediction ◽

Speed Prediction

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Comparison of feature extraction and normalization methods for speaker recognition using grid-audiovisual database

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v18.i2.pp782-789 ◽

2020 ◽

Vol 18 (2) ◽

pp. 782

Author(s):

Musab T. S. Al-Kaltakchi ◽

Haithem Abd Al-Raheem Taha ◽

Mohanad Abd Shehab ◽

Mohamed A.M. Abdullah

Keyword(s):

Feature Extraction ◽

Speaker Recognition ◽

Speaker Identification ◽

Gaussian Mixture ◽

Identification Accuracy ◽

Identification System ◽

Good Representation ◽

Mel Frequency Cepstral Coefficients ◽

Normalization Methods ◽

Cepstral Coefficients

<p><span lang="EN-GB">In this paper, different feature extraction and feature normalization methods are investigated for speaker recognition. With a view to give a good representation of acoustic speech signals, Power Normalized Cepstral Coefficients (PNCCs) and Mel Frequency Cepstral Coefficients (MFCCs) are employed for feature extraction. Then, to mitigate the effect of linear channel, Cepstral Mean-Variance Normalization (CMVN) and feature warping are utilized. The current paper investigates Text-independent speaker identification system by using 16 coefficients from both the MFCCs and PNCCs features. Eight different speakers are selected from the GRID-Audiovisual database with two females and six males. The speakers are modeled using the coupling between the Universal Background Model and Gaussian Mixture Models (GMM-UBM) in order to get a fast scoring technique and better performance. The system shows 100% in terms of speaker identification accuracy. The results illustrated that PNCCs features have better performance compared to the MFCCs features to identify females compared to male speakers. Furthermore, feature wrapping reported better performance compared to the CMVN method. </span></p>

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The Teager-Kaiser Energy Cepstral Coefficients as an Effective Structural Health Monitoring Tool

Applied Sciences ◽

10.3390/app9235064 ◽

2019 ◽

Vol 9 (23) ◽

pp. 5064 ◽

Cited By ~ 5

Author(s):

Marco Civera ◽

Matteo Ferraris ◽

Rosario Ceravolo ◽

Cecilia Surace ◽

Raimondo Betti

Keyword(s):

Experimental Data ◽

Structural Health Monitoring ◽

Health Monitoring ◽

Speech Processing ◽

Speaker Recognition ◽

Vibration Analysis ◽

Monitoring Tool ◽

Mel Frequency Cepstral Coefficients ◽

Structural Health ◽

Cepstral Coefficients

Recently, features and techniques from speech processing have started to gain increasing attention in the Structural Health Monitoring (SHM) community, in the context of vibration analysis. In particular, the Cepstral Coefficients (CCs) proved to be apt in discerning the response of a damaged structure with respect to a given undamaged baseline. Previous works relied on the Mel-Frequency Cepstral Coefficients (MFCCs). This approach, while efficient and still very common in applications, such as speech and speaker recognition, has been followed by other more advanced and competitive techniques for the same aims. The Teager-Kaiser Energy Cepstral Coefficients (TECCs) is one of these alternatives. These features are very closely related to MFCCs, but provide interesting and useful additional values, such as e.g., improved robustness with respect to noise. The goal of this paper is to introduce the use of TECCs for damage detection purposes, by highlighting their competitiveness with closely related features. Promising results from both numerical and experimental data were obtained.

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