Mid Sweden University

Direction of arrival estimation using the spherical microphone array usually requires a search in the whole 3-dimensional space, hence computationally demanding. This work presents a machine learning approach to sectorizing the 3-dimensional space, as an intermediate step for direction-of-arrival estimation using spherical microphone array. A new feature based on the outer product of spherical harmonic vectors was proposed for the classification. This spherical harmonic matrix nominally offers lower dimensionality compared to the commonly used covariance matrix of received data. The dimension of the input matrix was further reduced using the neighborhood component analysis. The extracted features were then used to train a support vector machine (SVM), 2-layer multilayer perceptron (MLP) and a convolutional neural network (CNN) for classification purposes. The results show that the models were able to classify the spherical sector with up to 90 % accuracy for all models and number of sectors under consideration. Also, the MLP and CNN trained with simulated samples were able to accurately classify samples from real data that were not included in training samples.

The field of Metrology has seen great use of Machine Learning and Deep Learning models, improving existing Metrology and enabling measurements and estimations that were previously not possible. In the challenging task of gathering training data in various areas of Metrology, a question arises; is it necessary to gather a completely new dataset every time a quality upgrade is done to a measurement system, method or model, or can the formerly trained model be used for new data with higher Signal-to-Noise Ratio (SNR)? This paper investigates how trained neural networks react to new data coming into the testing, with a higher SNR than the training data. In the experiments, Convolutional Neural Networks (CNN), in 1D and 2D, are used on heart sound data, as a test case. The initial results show that the classification accuracy for the new data, with a higher SNR, coming into the 1D CNN is almost as high as if the network had been trained on the higher SNR data. For a 2D CNN working with spectrograms instead of time series data, the change in accuracy is not nearly as high, as the 2D CNN model seems more robust to noise differences. 

Most biopolymers used as additives for the improvement of expansive subgrade soils are ecofriendly but highly uneconomical and unsustainable. Even the traditional additives such as cement, lime, and fly ash that are used widely for most soil improvement schemes are highly notorious for their carbon footprint. This necessitated the motivation in the present study to utilize an economical, ecofriendly and highly sustainable biopolymer, known as pregelatinized corn starch (PGCS), to improve the strength properties of an expansive subgrade soil. The PGCS was admixed with quarry dust (QD), an industrial waste additive, before blending with the expansive subgrade soil in different mix ratios generated with a 32 full factorial design experiment. The California bearing ratio (CBR) samples were subjected to 7 day curing while that of the unconfined compressive strength (UCS) were subjected to 1, 7, and 28 day curing. Shortly after the improvement of the expansive subgrade soil, the PGCS and QD were used as predictors in the development of two regression models for the two strength parameters (CBR and UCS) of the expansive subgrade soil considered in the study. Next, multiobjective salp swarm optimization algorithm (MOSSA), a bioinspired algorithm, was employed to optimize the additives in order to obtain optimal values of the strength properties of the expansive subgrade soil blended with the additives. The developed models were set as fitness functions in the slightly modified MOSSA technique. Thereafter, nondominated solutions were determined after the implementation of the optimization analysis. The results obtained from laboratory experiments and the optimization process showed that there was significant improvement in the UCS and CBR of the expansive subgrade soil. Optimal improvement in the UCS (1,326.241 kN/m2) and CBR (36.8%) were observed when an optimum mix ratio of the additives, 0.3117% PGCS and 10% QD, was blended with the expansive subgrade soil.

The present study aimed to assess the potential of a bio-inspired algorithm, multi-objective grey wolf optimization algorithm (MOGWO), to optimize the strength properties (California bearing ratio (CBR) and unconfined compressive strength (UCS)) of an expansive subgrade soil. This optimization process involves the use of two additives, namely a bio-polymer, pregelatinized corn starch (PGCS), and a nanoparticle agro waste, rice husk ash (RHA), blended with the soil in different mix ratios determined by a 32 factorial experimental design. The CBR samples were cured for 7 days, while the UCS samples were cured for 1, 7, and 28 days. To optimize the expansive subgrade soil strength, regression models were developed using PGCS and RHA as predictors for CBR and UCS, serving as fitness functions in the slightly modified MOGWO optimization technique. Next, the optimization analysis produced non-dominated solutions. The results obtained from the laboratory experiments and optimization analysis revealed that there was significant improvement in the UCS and CBR of the soil. These improvements can be attributed to the pozzolanic reaction between the soil-RHA matrix, the formation of intercalated and exfoliated nanocomposites, and the hydrophilic interaction of PGCS. By applying the slightly modified MOGWO technique, the study achieved optimal enhancements in UCS (710.3 kN/m2) and CBR (24.2%) when the expansive subgrade soil was mixed with 0.2637% PGCS and 12.2413% RHA. The results demonstrate the potential of the MOGWO technique in improving the properties of expansive subgrade soil. 

In cardiac auscultation, the ability to clearly hear any existing murmur sounds in heart sounds is crucial for proper diagnosis. This work aims to improve heart sound by the use of a stethoscope array and beamforming technique. The stethoscope array comprises four piezo elements for measurement, placed on the edges of a 40mm by 40mm rectangle. The directionality of the piezo elements reduces the effect of ambient noise in the measurement. The signal amelioration is achieved by isolating the systole and diastole sounds, and independently applying the delay-and-sum beamforming. This thereby makes any existing murmur sounds in the systole and/or diastole more audible and clearer to aid diagnosis. Finally, the designed stethoscope array and signal processing shows a gain of up to 33% for measured healthy heart samples, and up to 63% increase in murmur sound gain for measured sample with medically confirmed murmur. 

The joint and marginal probability densities of multipaths’ angles-of-arrival (AOA) and times-of-arrival (TOA) at the cellular base station are developed in closed form in this paper. Unlike the general simplification assumption in the open literature in which the scatterers are assumed to be located in a circular region for non-uniform spatial densities, the scatterers in this paper are assumed to be located in an elliptical region to properly model the elliptical footprint around the mobile station from the elevated base station with directional antenna. The inverted elliptic–parabolic spatial density was adopted to model the non-uniform distribution of the scatterers around the mobile. The uplink’s AOA–TOA joint distributions, AOA and TOA marginal distributions were analytically derived in closed form. How the eccentricity of the elliptical scatterer region affects the probability density functions is discussed. Furthermore, the derived AOA marginal distribution is compared to that of the elliptic conic and inverted parabolic models. The proposed model is shown to have better least-squares fit to some empirical AOA data compared to the elliptic conic and inverted parabolic models.

Traditional spherical sector microphone arrays using omnidirectional microphones face limitations in modal strength and spatial resolution, especially within spherical sector configurations. This study aims to enhance array performance by developing a spherical sector array employing first-order cardioid microphones. A model based on spherical sector harmonic (SSH) functions is introduced to extend the benefits of spherical harmonics to sector arrays. Modal strength analysis demonstrates that cardioid microphones in open spherical sectors enhance nonzero-order strengths and eliminate the nulls associated with spherical Bessel functions. We find that the spatial resolution of spherical cap arrays depends on the array’s maximum order and the limiting polar angle, but is independent of the microphone gain pattern. We assess direction-of-arrival (DOA) estimation performance for coherent wideband sources using the array manifold interpolation method, and compare cardioid and omnidirectional arrays through simulations in both open and rigid hemispherical configurations. The results indicate that cardioid arrays outperform omnidirectional ones on DOA estimation tasks, with performance improving alongside increased microphone directivity in the open hemispherical configuration. Specifically, hypercardioid microphones yielded the best results in the open configuration, while subcardioid microphones (without nulls) were optimal in rigid configurations. These findings demonstrate that spherical sector arrays of first-order cardioid microphones offer improved modal strength and DOA estimation capabilities over traditional omnidirectional arrays, providing significantly enhancing performance in spherical sector array processing.

Cardioid microphones/hydrophones have been commonplace in practical acoustics. Indeed, cardioid sensors are widely obtainable in retail as diverse commercial models produced by numerous manufacturers. A cardioid sensor has a highly directional gain pattern of &#x03B1; + (1 - &#x03B1;)cos(&#x03B3;), where &#x03B1; represents the sensor&#x2019;s cardioidicity index, and &#x03B3; denotes the impinging wavefront&#x2019;s spatial angle incident upon the cardioid sensor&#x2019;s axis. This paper is the first in the open literature to investigate the &#x201C;spatial matched filter" beam-pattern obtainable from two such cardioids that lie in perpendicular colocation. Such a bi-axial cardioid pair is worth investigating because (i) their diverse orientation successfully realizes two-dimensional (azimuth-polar) directivity with only two sensors, (ii) their spatial colocation is advantageous for <italic>physical compactness</italic> and also for the <italic>decoupling</italic> of the data&#x2019;s time-and-frequency dimensions from the azimuth-and-elevation directional dimensions to simplify signal-processing computation. This present investigation, however, uncovers that this beam-pattern generally peaks away from the nominal preset &#x201C;look direction", but algorithmic mitigation is made possible by this paper&#x2019;s pre-steering formula derived here to judiciously pre-adjust the beamformer&#x2019;s nominal &#x201C;look direction". Further action-able insights are provided in this paper for the hardware system engineer to pre-select the sensors&#x2019; cardioidicity index &#x03B1; for maximum steerability and freedom from sidelobes. 

Unequal “directivity orders” between two figure8 directional microphones/hydrophones are proposed and analyzed in this paper for beamforming. Specifically, two figure 8 sensors here, possibly of different “directivity orders”, are (i) orthogonally oriented to facilitate spatial filtering in both the azimuth and the elevation, and (ii) spatially colocated with an isotropic sensor (thereby decoupling the incident source's azimuth-elevation-radial dimensions from the impinging signal's time-frequency dimensions). This versatility (in the sensors’ diverse “directivity orders”) contrasts with all prior open literature on beamforming using a colocated triad of possibly directional sensors. Instead, this paper analytically explores how unequal directivity orders between two perpendicularly colocated figure-8 acoustic sensors would affect the triad array's “spatial matched filter” beam steering. The resulting “actionable” insights show which directivity-order combinations allow the beam of full maneuverability toward any azimuthal direction, and which directivity-order combinations only partial maneuverability.

In modern applications such as robotics, autonomous vehicles, and speaker localization, the computational power for sound source localization applications can be limited when other functionalities get more complex. In such application fields, there is a need to maintain high localization accuracy for several sound sources while reducing computational complexity. The array manifold interpolation (AMI) method applied with the Multiple Signal Classification (MUSIC) algorithm enables sound source localization of multiple sources with high accuracy. However, the computational complexity has so far been relatively high. This paper presents a modified AMI for uniform circular array (UCA) that offers reduced computational complexity compared to the original AMI. The complexity reduction is based on the proposed UCA-specific focusing matrix which eliminates the calculation of the Bessel function. The simulation comparison is done with the existing methods of iMUSIC, the Weighted Squared Test of Orthogonality of Projected Subspaces (WS-TOPS), and the original AMI. The experiment result under different scenarios shows that the proposed algorithm outperforms the original AMI method in terms of estimation accuracy and up to a 30% reduction in computation time. An advantage offered by this proposed method is the ability to implement wideband array processing on low-end microprocessors.