In response to the echo and noise interference problems faced by multi-user full-duplex
voice communication scenarios, this work proposes a complete solution from signal
modeling to beamforming optimization. First, by converting the Bluetooth channel
into a virtual acoustic channel, a comprehensive acoustic transmission model cover-
ing single-source and multi-source scenarios is established. On this basis, a variety
of beamforming and filtering strategies are discussed, and finally, the Multichannel
Wiener Filter (MWF) beamforming scheme is proposed. At the same time, for the
time-varying spatial domain, the update of the covariance matrix combined with Voice
Activity Detector (VAD) is discussed. The simulation shows that the MWF scheme
has good robustness and adaptability under different Bluetooth channel latency and
low Signal-to-Interference Ratio (SIR) environments; when the user end is equipped
with a sufficient number of microphones or there are many bystanders, the system
performance is further improved.

At the child brain facility at the Erasmus Medical Centre, multiple tests are performed with children who have one of several disorders. Two of these tests are done with electroencephalogram measurements and are called mismatch negativity and acoustic change complex. After a signal processing pipeline, the EEG measurements from these tests are shown as waveforms called event-related potentials. The goal of these measurements is to see if there is any relation between the waveforms and the disorder, age and other information about the subjects.\newline

These waveforms are measured for each subject, EEG electrode, and in the case of MMN, for different stimuli, which naturally results in a tensor data structure. Algorithms for discriminant analysis and regression that are developed to be applied to tensors are described and altered to take into account the properties of the EEG data. Discriminant analysis can be used to improve classification algorithms that distinguish disorders, while regression can be used to predict variables such as test scores based on the measured data. The algorithms are first tested on simulated data, which shows they can have some merit. Classification rates improve in most simulated cases when the discriminant analysis is applied to the data. Regression can also reliably predict variables when strong correlations are present between the input tensor and output variable. Based on the data from the child brain facility, the discriminant analysis still improves classification rates in some cases, but not as significantly as on the simulated data. Regression using the algorithms described in this thesis is not useful in predicting test scores from other experiments done with the subjects. \newline

The algorithms are also dissected to discover which specific features in the data tensor are weighted heavier by the algorithms. This is done to gain new insights into the differences between the disorders. When comparing the weights that are used for the simulated data with the features that are of importance, there is some relation, but not a very strong one. When the input tensor is however segmented in the time mode, the times of interest can be identified. The regression algorithm also resulted in weights that can be analysed to look at when and where the measurements relate to the output, but this did not show any interesting results. For real data, the segmented tensor resulted in some interesting insights about the differences between the various disorders.\newline

For the current dataset, the discriminant analysis algorithms do improve classification rates, but not by much. The features weighted the most by this algorithm in combination with a segmented tensor might give some insight into the disorders. The tensor regression methods do not work to predict a test score and do not give new insights into the disorders.

Introduction
Vocal expression of emotions is essential for social communication. Research has shown that children with impaired hearing have difficulties with accurately identifying emotions, which can limit their prosodic expression. Research on the ability of hearing-impaired children to express emotions through speech is limited. Moreover, existing studies have focused on evaluating basic prosodic features, rather than other important characteristics of speech, such as spectral features. This study aims to investigate differences in vocal emotion expression between children with normal hearing (NH children), children with cochlear implants (CI children) and children with hearing aids (HA children) with a more advanced speech analysis.

Methods
Two different analyses were performed. For the first analysis a machine learning (ML) model was developed to recognize emotions (happy, sad, angry) from speech. Prosodic (pitch, intensity and duration) and spectral features (MFCCs) were extracted from speech utterances. The model used a Support Vector Machine (SVM) classifier and was trained and tested for each group separately (NH children, CI children, HA children) and for all children with impaired hearing (HI children). For the second analysis pitch and intensity contours were made.

Results
A total of 828 speech utterances were used from 69 NH children, 420 from 35 CI children, and 624 from 52 HA children. The models achieved an overall accuracy of 57.3%, 35.7%, 32.3% and 34.6% for NH children, CI children, HA children and HI children respectively. Final model AUC-ROC values for NH children showed acceptable model performance, while AUC-ROC values for HI children showed poor performance, equivalent to random guessing. The contour analysis supported these outcomes. The pitch and intensity contours of NH children showed clear differentiation between emotions in contrast to CI and HA children.

Conclusion
The findings of this study highlighted significant differences in both global and local features of emotional expression in speech between NH children and HI children. ML models showed a better classification of emotions expressed by NH children compared to those with HI. Moreover, pitch and intensity contours showed that NH children exhibited distinct patterns when expressing emotions. In conclusion, HI children may face challenges in expressing emotions through speech

Atrial Fibrillation or AF is the most common heart rhythm anomaly affecting millions of people. This work explores the possibilities of reinterpreting speech processing techniques for use in atrial fibrillation detection. An existing method of modelling single heartbeat, single lead ECG signals by means of an ARMA model's amplitude response as a time domain signal is implemented. The parameters of the models are then used for AF detection by means of detecting P wave absence. For this detection, the distribution of the P wave associated parameters is compared to a GMM model of normal sinus rhythm beats obtained from a large number of recordings from different sources.

Atrial Fibrillation (AF) is a cardiac arrhythmia that occurs due to the initiation of electrical impulses in the heart in an uncoordinated manner, giving rise to a disorganized and rapid heartbeat. The progression of AF over time can lead to complications such as stroke and heart failure. Catheter ablation is an established treatment protocol adopted to eliminate AF triggers and restore normal sinus rhythm in patients. To improve the success rates of ablation procedures, mapping and localizing the regions in the heart that give rise to uncoordinated impulses is crucial. This work focuses on obtaining a visual estimate of the location of irregular electrical activity within a volume domain of cardiac tissue using epicardial electrogram measurements.
A straightforward, effective, and computationally fast backprojection imaging method is proposed that provides an indication of the regions in a given volume of cardiac tissue that exhibit prominent irregular atrial activity. The chosen volume is subdivided into non-overlapping voxels, and the standard electrogram signal model is discretized to a matrix multiplication form. The inverse problem of reconstructing the transmembrane current distribution in the volume is solved using the epicardial electrogram readings obtained from the surface and the inverse distances matrix containing the inverse of the distances from each electrode to each voxel within the volume domain. The generated backprojection images are visualized to estimate the location (x and y coordinate information) and depth of irregular atrial activity in the volume. To improve the accuracy of localization while maintaining the computationally fast nature of the proposed solution, Singular Value decomposition (SVD) of the inverse distances matrix is proposed for the transmembrane current reconstruction. The obtained visual estimates can guide ablation procedures, making them more targeted, and reducing the need for repeat ablation procedures due to AF recurrence.

Estimating the transmembrane currents travelling through the epicardium and local activation times based on atrial epicardial electrograms can greatly help in the study of cardiac arrhythmias such as atrial fibrillation. This work focuses on the accurate estimation of the aforementioned signals and features. To do this, two least squares-based regression methods were used to estimate transmembrane currents from electrograms and then find their local activation times by searching for the maximum negative slope. The first least squares optimization method consists of using standard least squares, while the second consists of regularized least squares, by combining both lasso and ridge regression, to deal with signal sparsity and multicollinearity, respectively. Furthermore, to improve estimation results, multiresolution analyses based on wavelet decompositions and principal components analysis were used to filter out parasitic components that were present in the estimated transmembrane currents by separating them from the main activation complex of the decomposed signals.
Using these algorithms on simulated data, it was shown that promising results can be achieved for both transmembrane current estimations and LAT estimations. Several wavelet support sizes were tested on the simulated data to observe performance changes. These were compared to an already existing LAT estimation algorithm. The results mainly confirm the efficiency of the proposed methods on severely diseased tissue corrupted by conduction blocks and noise.

Atrial fibrillation (AF) is the most commonly occurring arrhythmia in clinical practice and can have a significant impact on the current and future wellbeing of the patient. By placing an unipolar sensor array directly on the epicardium during an open-heart surgery to measure the electrical activity of the atrium, more insight about this disease can be obtained. One method to obtain these insights is by applying common signal processing models to the measured electrogram (EGM). This thesis further investigates this application and argues why the most common array processing signal models are fundamentally incompatible with this application and proposes two different signal models to rectify the identified discrepancies. The proposed signal models are subsequently analyzed and the conclusion is drawn that both novel signal models better fit the EGM signals, but one signal model in particular shows promising results. This potential is exemplified by using this signal model to formulate a novel LAT estimation technique that can compete with state of the art LAT estimation methods in terms of estimation accuracy and execution time. This result shows the potential of the proposed signal model and opens the door to explore more applications in the future.

For the heart to pump blood throughout the body, electrical impulses that trigger the cellular contraction must be generated and spread through the myocardial tissue. These signals propagate faster along the longitudinal cardiac fiber direction than the transverse direction, conferring the heart with anisotropic conduction properties. Therefore, the arrangement of the fibers within the tissue governs the impulse propagation. Given the variability of the fiber direction across the heart and between patients, incorporating it into electrophysiological models would enhance our understanding of the mechanisms and progression of different heart conditions, such as atrial fibrillation (AF). The study of this common cardiac arrhythmia relies on analyzing electrical recordings of the heart, known as electrograms (EGMs), which, if integrated with the patient’s fiber architecture into cardiac models, can enable effective personalized treatment. Over the years, researchers have proposed different approaches to estimate the fiber direction from EGMs. However, these methods have been evaluated in different, usually simplistic, cardiac tissue models, making their comparison, and therefore selection of the most accurate approach for clinical and research applications, challenging.

The current study aims to identify the best fiber direction estimation method under consistent and realistic conditions. To achieve this goal, synthetic EGMs and local activation time (LAT) maps were generated from 2D and 3D monodomain models that mimicked the muscle bundle, atrial bilayer, and ventricular transmural fiber rotation structures. A comparison analysis of existing fiber direction estimation methods, first as described by their authors and then standardized to have the same spatial resolution, showed the superior performance of the techniques based on fitting an ellipse to local conduction velocity or conduction slowness vectors from a whole LAT map. The estimation accuracy of these methods can be further improved by increasing the number of vectors to which the ellipse is fitted. Nonetheless, given the influence of underlying layers in the epicardial recordings, the estimation error increases in the tissue models where fibers in the epicardial and endocardial layers run perpendicularly. The effect on the estimate of such architecture, characteristic of the inferior side of the right atria and the ventricles, can be accounted for by combining epicardial electrical recordings obtained after pacing either in the endocardium or the epicardium. Although a preliminary assessment of the estimation methods was carried out with human EGMs, future studies should focus on validating the methods in a controlled experimental framework and refining them for more localized fiber direction estimation. All in all, the automation of the techniques and their integration into electrophysiological models brings us a step closer to creating valuable clinical tools for diagnosing and treating electropathologies.

Atrial fibrillation (AF) is one of the most common heart diseases. Billions of people have suffered from it in the world. Although it can lead to terrible complications such as stroke and heart failure, the underlying mechanisms of it are still under-explored. Besides, there is no so-called optimal therapy for the patients. As the disease is progressive, it is important to detect it in an early stage. To develop methods for understanding and detecting AF, the interpretable parametric model can be an option. This model can provide physiological information at the signal level. In this case, the electrocardiogram, as the most commonly used invasive measurement of cardiac conditions, can be the data to model the heart structure and cardiac activities.
This thesis proposes an interpretable parametric model based on P-waves extracted from the ECG signals. Specifically, the autoregressive (AR) model is implemented, which is also known as linear predicting coding (LPC). The goal is to model the atrium and understand the function of the atrium, which can reflect on the varying parameters in the SR and AF cases. In this context, The formant of P-waves is modeled and estimated, which is a representation of the atrium activities. In addition, the parameters of the model are mapped into 2-dimension by the zero-pole plots in order to interpret the differences between SR and AF situations. Based on the differences between parameters and formants, a parametric classifier of high interpretability is developed to detect AF. An alternating searching algorithm is proposed to determine the parameters of the classifier.

Atrial fibrillation is a common cardiovascular disease, affecting the regular beating of the heart through chaotic contraction of the heart's upper chambers. On its own, the condition—increasingly prevalent among the elderly—is not life threatening, but it leads to an increased risk of stroke and heart failure. As of yet, there is no consensus on the physiological mechanisms responsible for initiating and sustaining atrial fibrillation. A more detailed view of cardiac activity would improve understanding of the disease, making earlier diagnosis possible and improving options for treatment.

The contraction of the cardiac muscles is governed by electrical signals propagating through the tissue. Cardiac activity can be monitored with a high spatial resolution by measuring the electrical potential directly on the epicardium of the heart during open-heart surgery, using an array of closely spaced electrodes. From these electrograms, estimating the time of local activation of the cardiac tissue underneath each electrode provides a quantitative way of mapping the mechanisms of atrial fibrillation. Various methods exist to estimate the activation times, but the complex signals that are typical of atrial fibrillation make it difficult to obtain accurate results. This thesis proposes combining two existing methods for estimating the local activation times. Based on a model of the electrogram as a spatial convolution of local transmembrane currents, an inverse problem is formulated and solved, resulting in a less opaque view of the cardiac activity at the electrode locations by attenuating distant disturbances and emphasizing local activity. The deconvolution output is fed to the second step, where cross-correlating certain pairs of signals gives an estimate for the mutual time delay in local activation. A graph representation of the electrode array is used to define neighbor order and decide which signal pairs are correlated. The set of pairwise time delays this produces is then converted to an estimate for the local activation times, using a least-squares estimator.

The proposed method is evaluated using different simulated cardiac settings. In a setting with one stimulation source, earlier results of the deconvolution and cross-correlation methods are confirmed, and the proposed method is seen to produce a slightly lower mean error than reference methods. In the higher-complexity triple-source setting, the latter effect is again visible. Reinforced by the performance of the different methods in increasingly noisy settings, the main merits of the proposed method for the estimation of local activation times can be said to be found in the form of increased consistency, not significantly improving on the accuracy of existing methods.

The heart is one of our vital organs. It functions by periodically contracting in a rhythmic way. Sometimes, this rhythmic behaviour is affected by abnormalities in the tissue. These conditions are referred to as cardiac arrhythmias. One kind is of specific interest, called atrial fibrillation (AF). To further study AF, methods have been developed to estimate the conductivity of cardiac cells based on the measured electrical signals. In addition, other parameters of interest besides the conductivity are involved. The goal of this project was to consider one of the parameters in the electrogram (EGM) model: the sensor-to-cell height.
First, we studied the effect of the height as a parameter in the model when used for conductivity estimation. To that end, a detector was built with which we can explain the effect of all involved parameters on the ability to accurately estimate any parameters of interest.
In addition, we considered the case where the height is unknown and is estimated, thus possibly including estimation errors. The focus was on the consequences of making errors in the estimation of the height with respect to conduction block detection and conductivity estimation.
Lastly, the effort was made to estimate the height. Here, the optimisation problem of height estimation was formalised and derived as its implementable form. At first, a simplified EGM model was assumed in order to mimic and estimate cell-specific effects, i.e. the cell conductivities. Then, the height was estimated in various situations to study its behaviour and performance under different conditions. Then, also the standard EGM model was used in the same way, after which we also tested the performance of the designed algorithm in combination with existing conductivity estimation methods.

Being able to estimate atrial tissue conductivity parameters from epicardial electrograms is an important tool in diagnosing and treating heart rhythm disorders such as atrial fibrillation. One of these parameters is the atrial fibre direction, which is often assumed to be known in conductivity estimation methods. In this thesis, a novel method to estimate the fibre direction from epicardial electrograms is presented. This method is based on local conduction slowness vectors of a propagating activation wave, which can be calculated from a corresponding activation map of the atrial tissue. These conduction slowness vectors follow an elliptical pattern that strongly depends on the underlying conductivity parameters. The fibre direction and conductivity anisotropy ratio can therefore be estimated by fitting an ellipse to the conduction slowness vectors. Applying the presented method on simulated data shows that it can accurately estimate the fibre direction, and that the performance of the method depends mostly on the range of wavefront directions present in the measurement area. The main advantage of the presented method over existing methods is that it still functions in the presence of conduction blocks, as long as the surrounding tissue is approximately homogeneous.

The most common serious heart rhythm disease is atrial fibrillation. It is not fatal on its own but does increase the risk of heart failures and strokes. There is little understanding about the mechanisms behind the disease, so more insight is desired. Using an array of electrodes, measurements are being performed of the electrical atrial activity directly on the heart tissue. These signals are, however, not clean and suffer from far-field interference coming from the ventricles.

During normal sinus rhythm these atrial and ventricular activities are separated in time and easy to distinguish. In case of atrial fibrillation this is not always the case. Luckily, there is a major difference between both signals: the ventricular signal comes from far away and arrives therefore approximately simultaneously at all electrodes. A simple, but effective way to remove this ventricular activity is to use a bipolar electrode. It produces the difference between two normal unipolar electrodes, thus removing the common ventricular signal component.

The bipolar electrode, however, distorts the atrial signal component, which in some orientations can even lead to removing it altogether. This bipolar electrode is known as a differential beamformer from the field of array signal processing. There are more complex beamformers that can keep the atrial component undistorted and therefore produce better results than the bipolar electrode.

This thesis proposes a Fourier-domain signal model for all available electrodes relying on an atrial and ventricular transfer function. It is possible to estimate these transfer functions from the data blindly. Three beamformers are derived utilizing the signal model and the transfer functions. The bipolar electrode is extended to multiple electrodes like the other beamformers as well.

Experiments with simulated data show that the complex beamformers indeed keep the atrial activity undistorted and are still able to remove the ventricular activity effectively when using multiple electrodes, except for very complex data where the signal model is not valid. For low numbers of electrodes the beamformers are not useful, they hardly remove the ventricular activity while keeping the atrial component undistorted, where the bipolar electrode does the opposite.

The electrograms are also used to estimate local activation times of the cells underneath the electrodes which says something about the health of the cardiac tissue. Besides the mentioned filtering, this thesis proposes a method to estimate those moments in time by looking at the time-domain version of the atrial transfer function, called the atrial impulse response. For simple data, it performs well compared to state-of-the-art methods, but for more complicated data, it does not.

Sound pressure varies over space and time. Knowledge about this exact behavior has many applications, e.g., room compensation, dereverberation and sound field reconstruction. Inside enclosures, the sound field is influenced by the surroundings, such as the geometry of the enclosure and the materials used.
Reconstructing a satisfying sound field in the whole enclosure by extrapolating from few measurements is thus not an obvious task. The sound field in a room can be represented by a weighted sum of room modes. Thus, we can estimate the room modes and compute the sound field from it. To estimate the room modes, compressive sensing literature uses on-the-grid, sparse reconstruction methods. However, these on-the-grid methods are known to suffer from basis mismatch. In this work, we investigate the use of a gridless framework for estimating room modes using atomic norm minimization, a gridless method. The advantage of this approach is that it does not suffer from this basis mismatch problem.
We derive a bound for the sound field reconstruction problem in a one-dimensional room with rigid walls and relate this to the frequency separation that is required by the atomic norm. We conclude that for perfect reconstruction of the room modes based on the investigated gridless approach, additional
prior knowledge about the signal model is required. For example, knowledge about the shape of the room modes can be used. We show how recovery is possible in a one-dimensional setting by exploiting both the structure of the sound field and the acquisition method.

Current speech-encoding strategies in cochlear implants (CI’s) do not provide their users with sufficient spectral resolution for complex listening tasks and pitch perception. This paper presents a new test that has been developed to assess pitch perception: the glide tone test (GTT). A total of 25 normal hearing (NH) participants were tested to find their just noticeable difference (JND) between glide tones with a fixed range but a different exponent. The stimuli used mimic the sound spectrum of a violin and have 20 harmonics added to the fundamental frequency (F0). This paper also investigated possible learning effects the test
might possess, evaluated user-friendliness and optimised the procedure. The paper presents the JND scores obtained by the NH subjects, as well as the new testing method itself. Suggestions for further experiments with the GTT are made.

The earplug development followed by ALPINE, a hearing protection company is a trial and error method. This method leads to high material wastage. It is also a time-consuming and expensive development process. So, the objective of this thesis is to ease the error earplug development process by implementing a prediction model. The research is steered towards understanding the factors that influence the sound dampening properties in an earplug, building the dataset, and analysing the data and regression models. The regression model must be able to predict the noise attenuation provided by an earplug based on the material and design specifications for which Gaussian Process Regression (GPR) model is used. With RBF kernel function and through hyperparameter optimization, the GPR model is trained and tested on datasets.

The main finding of this thesis is that noise attenuation provided by an earplug is highly subjective. Sound perception is crucial in earplug design. Even though the most ideal sound attenuation prediction model for earplugs can be developed, in the end, everything relies on individual sound perception. So, the prediction of noise attenuation in earplugs has a higher level of uncertainty. However, with accurate age, gender, and ethnicity information, the earplugs can be modelled and designed for each group and the sound attenuation prediction model can be developed to make predictions with higher certainty.

Public address (PA) systems are an integral part of every building. Their function as a safety and security system could benefit from adding recording capabilities. With such capabilities, people could be located during evacuation or announcements could be adjusted to room acoustics for better intelligibility. However, adding microphones to a PA system is too costly and inefficient to be feasible. This thesis evaluates the feasibility of using the loudspeakers already in place as recording devices to provide information to the system. To this end, a system using a single loudspeaker as both a playback and recording device is analysed, modelled and simulated. The results show that using a current measuring set-up with an analogue-to-digital converter capable of detecting a range of roughly 120 dB, a speech signal up to three metres in a cone of 120° from a loudspeaker can be successfully estimated in an office room with an announcement playing and background noise present. As the estimated signal is unknown to the system, the solution generalises to other signal types as well. A system with a single loudspeaker can be utilised for the use cases presented, and is therefore proven feasible. To increase the practicality of the system, it is recommended to continue the research in two main areas. The first area considers improving the quality of the recording and extending the range from which the system can produce accurate recordings. The second area considers evaluating the practical implementation of the system, by extending the single loudspeaker case to a multiple loudspeaker case and generalising the system to be readily implementable in a large variety of PA systems.

Loudspeakers are placed in an environment unknown to the loudspeaker designers. The room influences the acoustic experience for the user. Having information about the room makes it possible to better reproduce the sound field as intended. Using microphone measurements, the location of acoustic reflectors can be inferred. Current state-of-the-art methods for room boundary detection focus on a two-dimensional setting. Detection of arbitrary reflectors in three dimensions increase complexity due to practical limitations, i.e. the need for a spherical array and the increase of computational complexity. The presence of horizontal reflectors cause inaccuracy for wall detection due to model mismatch. Loudspeakers may not present an omnidirectional directivity pattern, as usually assumed in the literature, thus making the detection of acoustic reflectors in some directions more challenging.

In this thesis, a LiDAR sensor is added to a smart loudspeaker to improve wall detection accuracy and robustness. This is done in two ways.
First, the horizontal reflectors that are not present in the acoustic model are sought detected with the LiDAR sensor to enable elimination of their detrimental influence. Second, a method is proposed to compensate for the challenging regions for wall detection in highly directive loudspeakers, using the LiDAR sensor. Experimental results, evaluated in different simulated scenarios are shown for comparison of the proposed method and the state-of-the-art method, that exclusively uses acoustic information.

With the aid of an array of loudspeakers, sound zone algorithms seek to reproduce multiple distinct zones of audio inside an enclosure. Typical approaches determine the loudspeaker inputs by optimizing over a cost function that models the sound pressure inside the enclosure. However, recent methods propose cost functions that include a perceptual model of the human auditory system, which further models the perception of sound. This thesis investigates such an approach by proposing a framework within which sound zones are constructed through optimization over a perceptual model. The framework is used to propose two perceptual sound zone algorithms: unconstrained and constrained perceptual pressure matching. Simulations of the proposed algorithms and a reference algorithm are presented to determine the benefits of including auditory-perceptual information in sound zone algorithms. From this, it is found that the unconstrained perceptual approach outperforms the reference in terms of various perceptual measures. In addition, it is found that adding perceptual constraints to the optimization problem allows for control of sound zones which correlates well with other perceptual quality measures.