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

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

Efficient Noise Suppression for Robust Speech Recognition

Electrical EngineeringThis thesis addresses the issues of single microphone based noise estimation technique for speech recognition in noise environments. A lot of researches have been performed on the environmental noise estimation, however most of them require voice activity detector (VAD) for accurate estimation of noise characteristics. I propose two approaches for efficient noise estimation without VAD. The first approach aims at improving the conventional quantile-based noise estimation (QBNE). I fostered the QBNE by adjusting the quantile level (QL) according to the relative amount of added noise to the target speech. Basically, we assign two different QLs, i.e., binary levels, according to the measured statistical moment of log scale power spectrum at each frequency. The second approach is applying dual mixture parametric model in computing likelihoods of speech and non-speech classes. I used dual Gaussian mixture model (GMM) and Rayleigh mixture model (RMM) for the likelihoods. From the assumption that speech is generally uncorrelated to the environmental noises, the noise power spectrum can be estimated by using each mixture model parameter of speech absence class. I compared the proposed methods with the conventional QBNE and minimum statistics based method on a simple speech recognition task in various signal-to-noise ratio (SNR) levels. Based on the experimental results, the proposed methods are shown to be superior to the conventional methods.ope

A noise spectral estimation method based on VAD and recursive averaging using new adaptive parameters for non-stationary noise environments

金沢大学理工研究域　電子情報学系A noise spectral estimation method, which is used in spectral suppression noise cancellers, is proposed for highly non-stationary noise environments. Speech and non-speech frames are detected by using the entropy-based voice activity detector (VAD). An adaptive normalization parameter and a variable threshold are newly introduced for the VAD. They are very useful for rapid change in the noise spectrum and power. Furthermore, a recursive averaging method is applied to estimating the noise spectrum in the non-speech frames. In this method, an adaptive smoothing parameter is proposed, based on speech presence probability. Simulations are carried out by using many kinds of noises, including white, babble, car, pink, factory and tank, which are changed from one to the other. The segmental SNR is improved by 2.0 ~3.8dB, and noise spectral estimation error is improved by 3.2 ~ 4.7dB for the white noise and the babble noise, which are changed from one to the other

Spectral Analysis for Signal Detection and Classification : Reducing Variance and Extracting Features

Spectral analysis encompasses several powerful signal processing methods. The papers in this thesis present methods for finding good spectral representations, and methods both for stationary and non-stationary signals are considered. Stationary methods can be used for real-time evaluation, analysing shorter segments of an incoming signal, while non-stationary methods can be used to analyse the instantaneous frequencies of fully recorded signals. All the presented methods aim to produce spectral representations that have high resolution and are easy to interpret. Such representations allow for detection of individual signal components in multi-component signals, as well as separation of close signal components. This makes feature extraction in the spectral representation possible, relevant features include the frequency or instantaneous frequency of components, the number of components in the signal, and the time duration of the components. Two methods that extract some of these features automatically for two types of signals are presented in this thesis. One adapted to signals with two longer duration frequency modulated components that detects the instantaneous frequencies and cross-terms in the Wigner-Ville distribution, the other for signals with an unknown number of short duration oscillations that detects the instantaneous frequencies in a reassigned spectrogram. This thesis also presents two multitaper methods that reduce the influence of noise on the spectral representations. One is designed for stationary signals and the other for non-stationary signals with multiple short duration oscillations. Applications for the methods presented in this thesis include several within medicine, e.g. diagnosis from analysis of heart rate variability, improved ultrasound resolution, and interpretation of brain activity from the electroencephalogram

Adaptive Hidden Markov Noise Modelling for Speech Enhancement

A robust and reliable noise estimation algorithm is required in many speech enhancement systems. The aim of this thesis is to propose and evaluate a robust noise estimation algorithm for highly non-stationary noisy environments. In this work, we model the non-stationary noise using a set of discrete states with each state representing a distinct noise power spectrum. In this approach, the state sequence over time is conveniently represented by a Hidden Markov Model (HMM). In this thesis, we first present an online HMM re-estimation framework that models time-varying noise using a Hidden Markov Model and tracks changes in noise characteristics by a sequential model update procedure that tracks the noise characteristics during the absence of speech. In addition the algorithm will when necessary create new model states to represent novel noise spectra and will merge existing states that have similar characteristics. We then extend our work in robust noise estimation during speech activity by incorporating a speech model into our existing noise model. The noise characteristics within each state are updated based on a speech presence probability which is derived from a modified Minima controlled recursive averaging method. We have demonstrated the effectiveness of our noise HMM in tracking both stationary and highly non-stationary noise, and shown that it gives improved performance over other conventional noise estimation methods when it is incorporated into a standard speech enhancement algorithm

Behavioral and neural selectivity for acoustic signatures of vocalizations

Vocal communication relies on the ability of listeners to identify, process, and respond to vocal sounds produced by others in complex environments. In order to accurately recognize these signals, animals’ auditory systems must robustly represent acoustic features that distinguish vocal sounds from other environmental sounds. In this dissertation, I describe experiments combining acoustic, behavioral, and neurophysiological approaches to identify behaviorally relevant vocalization features and understand how they are represented in the brain. First, I show that vocal responses to communication sounds in songbirds depend on the presence of specific spectral signatures of vocalizations. Second, I identify an anatomically localized neural population in the auditory cortex that shows selective responses for behaviorally relevant sounds. Third, I show that these neurons’ spectral selectivity is robust to acoustic context, indicating that they could function as spectral signature detectors in a variety of listening conditions. Last, I deconstruct neural selectivity for behaviorally relevant sounds and show that it is driven by a sensitivity to deep fluctuations in power along the sound frequency spectrum. Together, these results show that the processing of behaviorally relevant spectral features engages a specialized neural population in the auditory cortex, and elucidate an acoustic driver of vocalization selectivity

Speech Endpoint Detection: An Image Segmentation Approach

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

Improved methods for noise spectral estimation and adaptive spectral gain control in noise spectral suppressor

金沢大学理工研究域　電子情報学系In this paper, new approaches to noise spectrum estimation and spectral gain control are proposed for noise spectral suppressors. First, the speech absent frames are detected by using spectral entropy. In the speech absent frames, a weighting factor used in estimating the noise spectrum is modified so as to emphasize effect of the noisy speech signal. Next, a spectral gain is more reduced by multiplying a factor in order to suppress effects of the noise in the speech absent frames. Furthermore, in the speech present frames, in order to reduce signal distortion, the spectral gain is controlled to be unity based on an SNR calculated by using a ridgeline spectrum. Finally, the original noisy speech is added to the estimated speech in some ratio. This ratio is controlled by the long term averaged SNR of the estimated noise and the noisy speech. Computer simulations by using speech signals, the white noise, the car noise and the bubble noise, which are available in public, have been carried out for the conventional methods and the proposed method. The proposed method can improve a segmental SNR and speech quality compared to the conventional methods. Especially, it is useful for the bubble noise. ©2007 IEEE