The fractal characterisation of phonetic elements of human speech

The use of fractal techniques and fractal dimensions as a means of speech characterisation and speech recognition is a relatively new concept and as such very few papers have addressed the possibilities of its use and associated advantages and disadvantages over conventional methods. This thesis demonstrates that fractal techniques can effectively be used as a method of broad recognition of phonetic elements in human speech. Three distinct fractal methods have been used to associate fractal dimensions with speech: the Box Counting method, the Divider or Richardson method and the Minkowski-Bouligand disc method. Speech has been recorded by myself and another male and female speaker to provide a database of phonetic recordings that could be experimented on. The three fractal techniques were emulated by means of software programs written in a high level language

A Nonlinear Mixture Autoregressive Model For Speaker Verification

In this work, we apply a nonlinear mixture autoregressive (MixAR) model to supplant the Gaussian mixture model for speaker verification. MixAR is a statistical model that is a probabilistically weighted combination of components, each of which is an autoregressive filter in addition to a mean. The probabilistic mixing and the datadependent weights are responsible for the nonlinear nature of the model. Our experiments with synthetic as well as real speech data from standard speech corpora show that MixAR model outperforms GMM, especially under unseen noisy conditions. Moreover, MixAR did not require delta features and used 2.5x fewer parameters to achieve comparable or better performance as that of GMM using static as well as delta features. Also, MixAR suffered less from overitting issues than GMM when training data was sparse. However, MixAR performance deteriorated more quickly than that of GMM when evaluation data duration was reduced. This could pose limitations on the required minimum amount of evaluation data when using MixAR model for speaker verification

An exploration of what attracts leaders to city manager positions and how city managers have adapted in their positions

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Nonlinear Time-Frequency Control Theory with Applications

Nonlinear control is an important subject drawing much attention. When a nonlinear system undergoes route-to-chaos, its response is naturally bounded in the time-domain while in the meantime becoming unstably broadband in the frequency-domain. Control scheme facilitated either in the time- or frequency-domain alone is insufficient in controlling route-to-chaos, where the corresponding response deteriorates in the time and frequency domains simultaneously. It is necessary to facilitate nonlinear control in both the time and frequency domains without obscuring or misinterpreting the true dynamics. The objective of the dissertation is to formulate a novel nonlinear control theory that addresses the fundamental characteristics inherent of all nonlinear systems undergoing route-to-chaos, one that requires no linearization or closed-form solution so that the genuine underlying features of the system being considered are preserved. The theory developed herein is able to identify the dynamic state of the system in real-time and restrain time-varying spectrum from becoming broadband. Applications of the theory are demonstrated using several engineering examples including the control of a non-stationary Duffing oscillator, a 1-DOF time-delayed milling model, a 2-DOF micro-milling system, unsynchronized chaotic circuits, and a friction-excited vibrating disk. Not subject to all the mathematical constraint conditions and assumptions upon which common nonlinear control theories are based and derived, the novel theory has its philosophical basis established in the simultaneous time-frequency control, on-line system identification, and feedforward adaptive control. It adopts multi-rate control, hence enabling control over nonstationary, nonlinear response with increasing bandwidth ? a physical condition oftentimes fails the contemporary control theories. The applicability of the theory to complex multi-input-multi-output (MIMO) systems without resorting to mathematical manipulation and extensive computation is demonstrated through the multi-variable control of a micro-milling system. The research is of a broad impact on the control of a wide range of nonlinear and chaotic systems. The implications of the nonlinear time-frequency control theory in cutting, micro-machining, communication security, and the mitigation of friction-induced vibrations are both significant and immediate

Characterization of Healthy and Pathological Voice Through Measures Based on Nonlinear Dynamics

In this paper, we propose to quantify the quality of the recorded voice through objective nonlinear measures. Quantification of speech signal quality has been traditionally carried out with linear techniques since the classical model of voice production is a linear approximation. Nevertheless, nonlinear behaviors in the voice production process have been shown. This paper studies the usefulness of six nonlinear chaotic measures based on nonlinear dynamics theory in the discrimination between two levels of voice quality: healthy and pathological. The studied measures are first- and second-order Renyi entropies, the correlation entropy and the correlation dimension. These measures were obtained from the speech signal in the phase-space domain. The values of the first minimum of mutual information function and Shannon entropy were also studied. Two databases were used to assess the usefulness of the measures: a multiquality database composed of four levels of voice quality (healthy voice and three levels of pathological voice); and a commercial database (MEEI Voice Disorders) composed of two levels of voice quality (healthy and pathological voices). A classifier based on standard neural networks was implemented in order to evaluate the measures proposed. Global success rates of 82.47% (multiquality database) and 99.69% (commercial database) were obtained.Publicad

Permutation entropy and irreversibility in gait kinematic time series from patients with mild cognitive decline and early alzheimer’s dementia

Gait is a basic cognitive purposeful action that has been shown to be altered in late stages of neurodegenerative dementias. Nevertheless, alterations are less clear in mild forms of dementia, and the potential use of gait analysis as a biomarker of initial cognitive decline has hitherto mostly been neglected. Herein, we report the results of a study of gait kinematic time series for two groups of patients (mild cognitive impairment and mild Alzheimer’s disease) and a group of matched control subjects. Two metrics based on permutation patterns are considered, respectively measuring the complexity and irreversibility of the time series. Results indicate that kinematic disorganisation is present in early phases of cognitive impairment; in addition, they depict a rich scenario, in which some joint movements display an increased complexity and irreversibility, while others a marked decrease. Beyond their potential use as biomarkers, complexity and irreversibility metrics can open a new door to the understanding of the role of the nervous system in gait, as well as its adaptation and compensatory mechanismsThis research was funded through the Premio del Ilustre Colegio Profesional de Fisioterapeutas de la Comunidad De Madrid, prize number ICPFM-IX-201

2020 Conference Abstracts: Annual Undergraduate Research Conference at the Interface of Biology and Mathematics

Schedule and abstract book for the Twelfth Annual Undergraduate Research Conference at the Interface of Biology and Mathematics Date: October 31 - November 1, 2020Location: The 2020 conference was conducted remotely due to COVID-19 concerns, utilizing the sococo platform that allows personal avatars to move between rooms and sessions, interact in small groups and also participate in zoom sessions.Keynote Speaker: Gerardo Chowell, Population Health Sciences, Georgia State Univ. School of Public Health, AtlantaFeatured Speaker: Olivia Prosper, Mathematics, Univ. of Tennessee, Knoxvill

Acoustic Feature Identification to Recognize Rag Present in Borgit

In the world of Indian classical music, raga recognition is a crucial undertaking. Due to its particular sound qualities, the traditional wind instrument known as the borgit presents special difficulties for automatic raga recognition. In this research, we investigate the use of auditory feature identification methods to create a reliable raga recognition system for Borgit performances. Each of the Borgits, the devotional song of Assam is enriched with rag and each rag has unique melodious tune. This paper has carried out few experiments on the audio samples of rags and a few Borgits sung with those rugs. In this manuscript three mostly used rags and a few Borgits  with these rags are considered for the experiment. Acoustic features considred here are FFT (Fast Fourier Transform), ZCR (Zero Crossing Rates), Mean and Standard deviation of pitch contour and RMS(Root Mean Square). After evaluation and analysis it is seen that FFT  and ZCR are two noteworthy acoustic features that helps to identify the rag present in Borgits. At last K-means clustering was applied on the FFT and ZCR values of the Borgits and were able to find correct grouping according to rags present there. This research validates FFT and ZCR as most precise acoustic parameters for rag identification in Borgit. Here researchers had observed roles of Standard deviation of pitch contour and RMS values of the audio samples in rag identification. &nbsp