2,414 research outputs found
Subband Adaptive Modeling of Digital Hearing Aids
In this thesis, the application of a subband adaptive model to characterize compression behaviour of five digital hearing aids is investigated. Using a signal-to-error ratio metric, modeling performance is determined by varying the number of analysis bands in the subband structure as well as consideration of three adaptive algorithms. The normalized least mean-squares (NLMS), the affine projection algorithm (APA), and the recursive least-squares (RLS) algorithms are employed using a range of parameters to determine the impact on modeling performance. Using the subband adaptive model to estimate the time-varying frequency response of each hearing aid allows the Perceptual Evaluation of Speech Quality (PESQ) mean-opinion score (MOS) to be computed. The PESQ MOS facilitates an estimation of a subjective assessment of speech quality using an objective score. Initial results suggest the PESQ MOS score is able to differentiate speech processed by hearing aids allowing them to be ranked accordingly. Further work is required to obtain subjective assessments of the processed speech signals and determine if possible correlations exist
Towards accurate estimation of fast varying frequency in future electricity networks: The transition from model-free methods to model-based approach
Accurate estimation of fast varying fundamental frequency in the presence of harmonics and noise will be required for effective frequency regulation in future electricity networks with high penetration level of renewable energy sources. Two new algorithms for network frequency tracking are proposed. The first algorithm represents a robust modification of classical zero crossing method, which is widely used in industry. The second algorithm is a multiple model algorithm based on the systems with harmonic regressor. Algorithm allows complete reconstruction of the frequency content of the signal, using information about the upper bound of the number of harmonics only. Moreover, new family of high-order algorithms together with new stepwise splitting method are proposed for parameter calculation in systems with harmonic regressor for the accuracy improvement. Statistical methods are introduced for comparison of two new algorithms to classical zero crossing algorithm. The modified algorithm provides significant improvement compared to the classical algorithm, and the algorithm with harmonic regressor provides further improvement of the statistical performance indexes with respect to the modified algorithm
Real-time detection of auditory : steady-state brainstem potentials evoked by auditory stimuli
The auditory steady-state response (ASSR) is advantageous against other hearing techniques because of its capability in providing objective and frequency specific information. The objectives are to reduce the lengthy test duration, and improve the signal detection rate and the robustness of the detection against the background noise and unwanted artefacts.Two prominent state estimation techniques of Luenberger observer and Kalman filter have been used in the development of the autonomous ASSR detection scheme. Both techniques are real-time implementable, while the challenges faced in the application of the observer and Kalman filter techniques are the very poor SNR (could be as low as â30dB) of ASSRs and unknown statistics of the noise. Dual-channel architecture is proposed, one is for the estimate of sinusoid and the other for the estimate of the background noise. Simulation and experimental studies were also conducted to evaluate the performances of the developed ASSR detection scheme, and to compare the new method with other conventional techniques. In general, both the state estimation techniques within the detection scheme produced comparable results as compared to the conventional techniques, but achieved significant measurement time reduction in some cases. A guide is given for the determination of the observer gains, while an adaptive algorithm has been used for adjustment of the gains in the Kalman filters.In order to enhance the robustness of the ASSR detection scheme with adaptive Kalman filters against possible artefacts (outliers), a multisensory data fusion approach is used to combine both standard mean operation and median operation in the ASSR detection algorithm. In addition, a self-tuned statistical-based thresholding using the regression technique is applied in the autonomous ASSR detection scheme. The scheme with adaptive Kalman filters is capable of estimating the variances of system and background noise to improve the ASSR detection rate
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
Methods of Optimizing Speech Enhancement for Hearing Applications
Speech intelligibility in hearing applications suffers from background noise. One of the most effective solutions is to develop speech enhancement algorithms based on the biological traits of the auditory system. In humans, the medial olivocochlear (MOC) reflex, which is an auditory neural feedback loop, increases signal-in-noise detection by suppressing cochlear response to noise. The time constant is one of the key attributes of the MOC reflex as it regulates the variation of suppression over time. Different time constants have been measured in nonhuman mammalian and human auditory systems. Physiological studies reported that the time constant of nonhuman mammalian MOC reflex varies with the properties (e.g. frequency, bandwidth) changes of the stimulation. A human based study suggests that time constant could vary when the bandwidth of the noise is changed. Previous works have developed MOC reflex models and successfully demonstrated the benefits of simulating the MOC reflex for speech-in-noise recognition. However, they often used fixed time constants. The effect of the different time constants on speech perception remains unclear. The main objectives of the present study are (1) to study the effect of the MOC reflex time constant on speech perception in different noise conditions; (2) to develop a speech enhancement algorithm with dynamic time constant optimization to adapt to varying noise conditions for improving speech intelligibility. The first part of this thesis studies the effect of the MOC reflex time constants on speech-in-noise perception. Conventional studies do not consider the relationship between the time constants and speech perception as it is difficult to measure the speech intelligibility changes due to varying time constants in human subjects. We use a model to investigate the relationship by incorporating Meddisâ peripheral auditory model (which includes a MOC reflex) with an automatic speech recognition (ASR) system. The effect of the MOC reflex time constant is studied by adjusting the time constant parameter of the model and testing the speech recognition accuracy of the ASR. Different time constants derived from human data are evaluated in both speech-like and non-speech like noise at the SNR levels from -10 dB to 20 dB and clean speech condition. The results show that the long time constants (â„1000 ms) provide a greater improvement of speech recognition accuracy at SNR levelsâ€10 dB. Maximum accuracy improvement of 40% (compared to no MOC condition) is shown in pink noise at the SNR of 10 dB. Short time constants (<1000 ms) show recognition accuracy over 5% higher than the longer ones at SNR levels â„15 dB. The second part of the thesis develops a novel speech enhancement algorithm based on the MOC reflex with a time constant that is dynamically optimized, according to a lookup table for varying SNRs. The main contributions of this part include: (1) So far, the existing SNR estimation methods are challenged in cases of low SNR, nonstationary noise, and computational complexity. High computational complexity would increase processing delay that causes intelligibility degradation. A variance of spectral entropy (VSE) based SNR estimation method is developed as entropy based features have been shown to be more robust in the cases of low SNR and nonstationary noise. The SNR is estimated according to the estimated VSE-SNR relationship functions by measuring VSE of noisy speech. Our proposed method has an accuracy of 5 dB higher than other methods especially in the babble noise with fewer talkers (2 talkers) and low SNR levels (< 0 dB), with averaging processing time only about 30% of the noise power estimation based method. The proposed SNR estimation method is further improved by implementing a nonlinear filter-bank. The compression of the nonlinear filter-bank is shown to increase the stability of the relationship functions. As a result, the accuracy is improved by up to 2 dB in all types of tested noise. (2) A modification of Meddisâ MOC reflex model with a time constant dynamically optimized against varying SNRs is developed. The model incudes simulated inner hair cell response to reduce the model complexity, and now includes the SNR estimation method. Previous MOC reflex models often have fixed time constants that do not adapt to varying noise conditions, whilst our modified MOC reflex model has a time constant dynamically optimized according to the estimated SNRs. The results show a speech recognition accuracy of 8 % higher than the model using a fixed time constant of 2000 ms in different types of noise. (3) A speech enhancement algorithm is developed based on the modified MOC reflex model and implemented in an existing hearing aid system. The performance is evaluated by measuring the objective speech intelligibility metric of processed noisy speech. In different types of noise, the proposed algorithm increases intelligibility at least 20% in comparison to unprocessed noisy speech at SNRs between 0 dB and 20 dB, and over 15 % in comparison to processed noisy speech using the original MOC based algorithm in the hearing aid
Distributed adaptive signal processing for frequency estimation
It is widely recognised that future smart grids will heavily rely upon intelligent communication and signal processing as enabling technologies for their operation. Traditional tools for power system analysis, which have been built from a circuit theory perspective, are a good match for balanced system conditions. However, the unprecedented changes that are imposed by smart grid requirements, are pushing the limits of these old paradigms.
To this end, we provide new signal processing perspectives to address some fundamental operations in power systems such as frequency estimation, regulation and fault detection. Firstly, motivated by our finding that any excursion from nominal power system conditions results in a degree of non-circularity in the measured variables, we cast the frequency estimation problem into a distributed estimation framework for noncircular complex random variables. Next, we derive the required next generation widely linear, frequency estimators which incorporate the so-called augmented data statistics and cater for the noncircularity and a widely linear nature of system functions. Uniquely, we also show that by virtue of augmented complex statistics, it is possible to treat frequency tracking and fault detection in a unified way.
To address the ever shortening time-scales in future frequency regulation tasks, the developed distributed widely linear frequency estimators are equipped with the ability to compensate for the fewer available temporal voltage data by exploiting spatial diversity in wide area measurements. This contribution is further supported by new physically meaningful theoretical results on the statistical behavior of distributed adaptive filters. Our approach avoids the current restrictive assumptions routinely employed to simplify the analysis by making use of the collaborative learning strategies of distributed agents. The efficacy of the proposed distributed frequency estimators over standard strictly linear and stand-alone algorithms is illustrated in case studies over synthetic and real-world three-phase measurements.
An overarching theme in this thesis is the elucidation of underlying commonalities between different methodologies employed in classical power engineering and signal processing. By revisiting fundamental power system ideas within the framework of augmented complex statistics, we provide a physically meaningful signal processing perspective of three-phase transforms and reveal their intimate connections with spatial discrete Fourier transform (DFT), optimal dimensionality reduction and frequency demodulation techniques. Moreover, under the widely linear framework, we also show that the two most widely used frequency estimators in the power grid are in fact special cases of frequency demodulation techniques.
Finally, revisiting classic estimation problems in power engineering through the lens of non-circular complex estimation has made it possible to develop a new self-stabilising adaptive three-phase transformation which enables algorithms designed for balanced operating conditions to be straightforwardly implemented in a variety of real-world unbalanced operating conditions. This thesis therefore aims to help bridge the gap between signal processing and power communities by providing power system designers with advanced estimation algorithms and modern physically meaningful interpretations of key power engineering paradigms in order to match the dynamic and decentralised nature of the smart grid.Open Acces
Compact Digital Predistortion for Multi-band and Wide-band RF Transmitters
This thesis is focusing on developing a compact digital predistortion (DPD) system
which costs less DPD added power consumptions. It explores a new theory
and techniques to relieve the requirement of the number of training samples and
the sampling-rate of feedback ADCs in DPD systems. A new theory about the
information carried by training samples is introduced. It connects the generalized
error of the DPD estimation algorithm with the statistical properties of
modulated signals. Secondly, based on the proposed theory, this work introduces
a compressed sample selection method to reduce the number of training samples
by only selecting the minimal samples which satisfy the foreknown probability
information. The number of training samples and complex multiplication operations
required for coefficients estimation can be reduced by more than ten
times without additional calculation resource. Thirdly, based on the proposed
theory, this thesis proves that theoretically a DPD system using memory polynomial
based behavioural modes and least-square (LS) based algorithms can be
performed with any sampling-rate of feedback samples. The principle, implementation
and practical concerns of the undersampling DPD which uses lower
sampling-rate ADC are then introduced. Finally, the observation bandwidth of
DPD systems can be extended by the proposed multi-rate track-and-hold circuits
with the associated algorithm. By addressing several parameters of ADC
and corresponding DPD algorithm, multi-GHz observation bandwidth using only
a 61.44MHz ADC is achieved, and demonstrated the satisfactory linearization
performance of multi-band and continued wideband RF transmitter applications
via extensive experimental tests
Linear Operation of Switch-Mode Outphasing Power Amplifiers
Radio transceivers are playing an increasingly important role in modern society. The
âconnectedâ lifestyle has been enabled by modern wireless communications. The demand
that has been placed on current wireless and cellular infrastructure requires increased spectral
efficiency however this has come at the cost of power efficiency. This work investigates
methods of improving wireless transceiver efficiency by enabling more efficient power
amplifier architectures, specifically examining the role of switch-mode power amplifiers in
macro cell scenarios. Our research focuses on the mechanisms within outphasing power
amplifiers which prevent linear amplification. From the analysis it was clear that high power
non-linear effects are correctable with currently available techniques however non-linear effects
around the zero crossing point are not. As a result signal processing techniques for suppressing
and avoiding non-linear operation in low power regions are explored. A novel method of digital
pre-distortion is presented, and conventional techniques for linearisation are adapted for the
particular needs of the outphasing power amplifier. More unconventional signal processing
techniques are presented to aid linearisation of the outphasing power amplifier, both zero
crossing and bandwidth expansion reduction methods are designed to avoid operation in nonlinear
regions of the amplifiers. In combination with digital pre-distortion the techniques
will improve linearisation efforts on outphasing systems with dynamic range and bandwidth
constraints respectively.
Our collaboration with NXP provided access to a digital outphasing power amplifier,
enabling empirical analysis of non-linear behaviour and comparative analysis of behavioural
modelling and linearisation efforts. The collaboration resulted in a bench mark for linear
wideband operation of a digital outphasing power amplifier. The complimentary linearisation
techniques, bandwidth expansion reduction and zero crossing reduction have been evaluated in
both simulated and practical outphasing test benches. Initial results are promising and indicate
that the benefits they provide are not limited to the outphasing amplifier architecture alone.
Overall this thesis presents innovative analysis of the distortion mechanisms of the
outphasing power amplifier, highlighting the sensitivity of the system to environmental effects.
Practical and novel linearisation techniques are presented, with a focus on enabling wide band
operation for modern communications standards
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