43 research outputs found

    Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

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    In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe

    Speech enhancement by perceptual adaptive wavelet de-noising

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    This thesis work summarizes and compares the existing wavelet de-noising methods. Most popular methods of wavelet transform, adaptive thresholding, and musical noise suppression have been analyzed theoretically and evaluated through Matlab simulation. Based on the above work, a new speech enhancement system using adaptive wavelet de-noising is proposed. Each step of the standard wavelet thresholding is improved by optimized adaptive algorithms. The Quantile based adaptive noise estimate and the posteriori SNR based threshold adjuster are compensatory to each other. The combination of them integrates the advantages of these two approaches and balances the effects of noise removal and speech preservation. In order to improve the final perceptual quality, an innovative musical noise analysis and smoothing algorithm and a Teager Energy Operator based silent segment smoothing module are also introduced into the system. The experimental results have demonstrated the capability of the proposed system in both stationary and non-stationary noise environments

    Automatic speaker recognition: modelling, feature extraction and effects of clinical environment

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    Speaker recognition is the task of establishing identity of an individual based on his/her voice. It has a significant potential as a convenient biometric method for telephony applications and does not require sophisticated or dedicated hardware. The Speaker Recognition task is typically achieved by two-stage signal processing: training and testing. The training process calculates speaker-specific feature parameters from the speech. The features are used to generate statistical models of different speakers. In the testing phase, speech samples from unknown speakers are compared with the models and classified. Current state of the art speaker recognition systems use the Gaussian mixture model (GMM) technique in combination with the Expectation Maximization (EM) algorithm to build the speaker models. The most frequently used features are the Mel Frequency Cepstral Coefficients (MFCC). This thesis investigated areas of possible improvements in the field of speaker recognition. The identified drawbacks of the current speaker recognition systems included: slow convergence rates of the modelling techniques and feature’s sensitivity to changes due aging of speakers, use of alcohol and drugs, changing health conditions and mental state. The thesis proposed a new method of deriving the Gaussian mixture model (GMM) parameters called the EM-ITVQ algorithm. The EM-ITVQ showed a significant improvement of the equal error rates and higher convergence rates when compared to the classical GMM based on the expectation maximization (EM) method. It was demonstrated that features based on the nonlinear model of speech production (TEO based features) provided better performance compare to the conventional MFCCs features. For the first time the effect of clinical depression on the speaker verification rates was tested. It was demonstrated that the speaker verification results deteriorate if the speakers are clinically depressed. The deterioration process was demonstrated using conventional (MFCC) features. The thesis also showed that when replacing the MFCC features with features based on the nonlinear model of speech production (TEO based features), the detrimental effect of the clinical depression on speaker verification rates can be reduced

    Detection of clinical depression in adolescents' using acoustic speech analysis

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    Clinical depression is a major risk factor in suicides and is associated with high mortality rates, therefore making it one of the leading causes of death worldwide every year. Symptoms of depression often first appear during adolescence at a time when the voice is changing, in both males and females, suggesting that specific studies of these phenomena in adolescent populations are warranted. The properties of acoustic speech have previously been investigated as possible cues for depression in adults. However, these studies were restricted to small populations of patients and the speech recordings were made during patient’s clinical interviews or fixed-text reading sessions. A collaborative effort with the Oregon research institute (ORI), USA allowed the development of a new speech corpus consisting of a large sample size of 139 adolescents (46 males and 93 females) that were divided into two groups (68 clinically depressed and 71 controls). The speech recordings were made during naturalistic interactions between adolescents and parents. Instead of covering a plethora of acoustic features in the investigation, this study takes the knowledge based from speech science and groups the acoustic features into five categories that relate to the physiological and perceptual areas of the speech production mechanism. These five acoustic feature categories consisted of the prosodic, cepstral, spectral, glottal and Teager energy operator (TEO) based features. The effectiveness in applying these acoustic feature categories in detecting adolescent’s depression was measured. The salient feature categories were determined by testing the feature categories and their combinations within a binary classification framework. In consistency with previous studies, it was observed that: - there are strong gender related differences in classification accuracy; - the glottal features provide an important enhancement of the classification accuracy when combined with other types of features; An important new contribution provided by this thesis was to observe that the TEO based features significantly outperformed prosodic, cepstral, spectral, glottal features and their combinations. An investigation into the possible reasons of such strong performance of the TEO features pointed into the importance of nonlinear mechanisms associated with the glottal flow formation as possible cues for depression

    Détection d'activité vocale basée sur la transformée en ondelettes

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    Unsupervised Spectral Subtraction for Noise-Robust ASR on Unknown Transmission Channels

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    This paper addresses several issues of classical spectral subtraction methods with respect to the automatic speech recognition task in noisy environments. The main contributions of this paper are twofold. First, a channel normalization method is proposed to extend spectral subtraction to the case of transmission channels such as cellphones. It equalizes the transmission channel and removes part of the additive noise. Second, a simple, computationally efficient \mbox{2-component} probabilistic model is proposed to discriminate between speech and additive noise at the magnitude spectrogram level. Based on this model, an alternative to classical spectral subtraction is proposed, called ``Unsupervised Spectral Subtraction''~(USS). The main difference is that the proposed approach does not require any parameter tuning. Experimental studies on Aurora~2 show that channel normalization followed by USS compares advantageously to both classical spectral subtraction, and the ETSI standard front-end (Wiener filtering). Compared to the ETSI standard front-end, a 21.3\%~relative improvement is obtained on 0 to 20~dB noise conditions, for an absolute loss of 0.1~\% in clean conditions. The computational cost of the proposed approach is very low, which makes it fit for real-time applications

    Stress and emotion recognition in natural speech in the work and family environments

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    The speech stress and emotion recognition and classification technology has a potential to provide significant benefits to the national and international industry and society in general. The accuracy of an automatic emotion speech and emotion recognition relays heavily on the discrimination power of the characteristic features. This work introduced and examined a number of new linear and nonlinear feature extraction methods for an automatic detection of stress and emotion in speech. The proposed linear feature extraction methods included features derived from the speech spectrograms (SS-CB/BARK/ERB-AE, SS-AF-CB/BARK/ERB-AE, SS-LGF-OFS, SS-ALGF-OFS, SS-SP-ALGF-OFS and SS-sigma-pi), wavelet packets (WP-ALGF-OFS) and the empirical mode decomposition (EMD-AER). The proposed nonlinear feature extraction methods were based on the results of recent laryngological studies and nonlinear modelling of the phonation process. The proposed nonlinear features included the area under the TEO autocorrelation envelope based on different spectral decompositions (TEO-DWT, TEO-WP, TEO-PWP-S and TEO-PWP-G), as well as features representing spectral energy distribution of speech (AUSEES) and glottal waveform (AUSEEG). The proposed features were compared with features based on the classical linear model of speech production including F0, formants, MFCC and glottal time/frequency parameters. Two classifiers GMM and KNN were tested for consistency. The experiments used speech under actual stress from the SUSAS database (7 speakers; 3 female and 4 male) and speech with five naturally expressed emotions (neutral, anger, anxious, dysphoric and happy) from the ORI corpora (71 speakers; 27 female and 44 male). The nonlinear features clearly outperformed all the linear features. The classification results demonstrated consistency with the nonlinear model of the phonation process indicating that the harmonic structure and the spectral distribution of the glottal energy provide the most important cues for stress and emotion recognition in speech. The study also investigated if the automatic emotion recognition can determine differences in emotion expression between parents of depressed adolescents and parents of non-depressed adolescents. It was also investigated if there are differences in emotion expression between mothers and fathers in general. The experiment results indicated that parents of depressed adolescent produce stronger more exaggerated expressions of affect than parents of non-depressed children. And females in general provide easier to discriminate (more exaggerated) expressions of affect than males

    Speech Endpoint Detection: An Image Segmentation Approach

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    Speech Endpoint Detection, also known as Speech Segmentation, is an unsolved problem in speech processing that affects numerous applications including robust speech recognition. This task is not as trivial as it appears, and most of the existing algorithms degrade at low signal-to-noise ratios (SNRs). Most of the previous research approaches have focused on the development of robust algorithms with special attention being paid to the derivation and study of noise robust features and decision rules. This research tackles the endpoint detection problem in a different way, and proposes a novel speech endpoint detection algorithm which has been derived from Chan-Vese algorithm for image segmentation. The proposed algorithm has the ability to fuse multi features extracted from the speech signal to enhance the detection accuracy. The algorithm performance has been evaluated and compared to two widely used speech detection algorithms under various noise environments with SNR levels ranging from 0 dB to 30 dB. Furthermore, the proposed algorithm has also been applied to different types of American English phonemes. The experiments show that, even under conditions of severe noise contamination, the proposed algorithm is more efficient as compared to the reference algorithms

    Unveiling the frontiers of deep learning: innovations shaping diverse domains

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    Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table
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