Engineering systematic musicology : methods and services for computational and empirical music research

One of the main research questions of *systematic musicology* is concerned with how people make sense of their musical environment. It is concerned with signification and meaning-formation and relates musical structures to effects of music. These fundamental aspects can be approached from many different directions. One could take a cultural perspective where music is considered a phenomenon of human expression, firmly embedded in tradition. Another approach would be a cognitive perspective, where music is considered as an acoustical signal of which perception involves categorizations linked to representations and learning. A performance perspective where music is the outcome of human interaction is also an equally valid view. To understand a phenomenon combining multiple perspectives often makes sense. The methods employed within each of these approaches turn questions into concrete musicological research projects. It is safe to say that today many of these methods draw upon digital data and tools. Some of those general methods are feature extraction from audio and movement signals, machine learning, classification and statistics. However, the problem is that, very often, the *empirical and computational methods require technical solutions* beyond the skills of researchers that typically have a humanities background. At that point, these researchers need access to specialized technical knowledge to advance their research. My PhD-work should be seen within the context of that tradition. In many respects I adopt a problem-solving attitude to problems that are posed by research in systematic musicology. This work *explores solutions that are relevant for systematic musicology*. It does this by engineering solutions for measurement problems in empirical research and developing research software which facilitates computational research. These solutions are placed in an engineering-humanities plane. The first axis of the plane contrasts *services* with *methods*. Methods *in* systematic musicology propose ways to generate new insights in music related phenomena or contribute to how research can be done. Services *for* systematic musicology, on the other hand, support or automate research tasks which allow to change the scope of research. A shift in scope allows researchers to cope with larger data sets which offers a broader view on the phenomenon. The second axis indicates how important Music Information Retrieval (MIR) techniques are in a solution. MIR-techniques are contrasted with various techniques to support empirical research. My research resulted in a total of thirteen solutions which are placed in this plane. The description of seven of these are bundled in this dissertation. Three fall into the methods category and four in the services category. For example Tarsos presents a method to compare performance practice with theoretical scales on a large scale. SyncSink is an example of a service

Automatic Environmental Sound Recognition: Performance versus Computational Cost

In the context of the Internet of Things (IoT), sound sensing applications are required to run on embedded platforms where notions of product pricing and form factor impose hard constraints on the available computing power. Whereas Automatic Environmental Sound Recognition (AESR) algorithms are most often developed with limited consideration for computational cost, this article seeks which AESR algorithm can make the most of a limited amount of computing power by comparing the sound classification performance em as a function of its computational cost. Results suggest that Deep Neural Networks yield the best ratio of sound classification accuracy across a range of computational costs, while Gaussian Mixture Models offer a reasonable accuracy at a consistently small cost, and Support Vector Machines stand between both in terms of compromise between accuracy and computational cost

Dynamic spatial segmentation strategy based magnetic field indoor positioning system

In this day and age, it is imperative for anyone who relies on a mobile device to track and navigate themselves using the Global Positioning System (GPS). Such satellite-based positioning works as intended when in the outdoors, or when the device is able to have unobstructed communication with GPS satellites. Nevertheless, at the same time, GPS signal fades away in indoor environments due to the effects of multi-path components and obstructed line-of-sight to the satellite. Therefore, numerous indoor localisation applications have emerged in the market, geared towards finding a practical solution to satisfy the need for accuracy and efficiency. The case of Indoor Positioning System (IPS) is promoted by recent smart devices, which have evolved into a multimedia device with various sensors and optimised connectivity. By sensing the device’s surroundings and inferring its context, current IPS technology has proven its ability to provide stable and reliable indoor localisation information. However, such a system is usually dependent on a high-density of infrastructure that requires expensive installations (e.g. Wi-Fi-based IPS). To make a trade-off between accuracy and cost, considerable attention from many researchers has been paid to the range of infrastructure-free technologies, particularly exploiting the earth’s magnetic field (EMF). EMF is a promising signal type that features ubiquitous availability, location specificity and long-term stability. When considering the practicality of this typical signal in IPS, such a system only consists of mobile device and the EMF signal. To fully comprehend the conventional EMF-based IPS reported in the literature, a preliminary experimental study on indoor EMF characteristics was carried out at the beginning of this research. The results revealed that the positioning performance decreased when the presence of magnetic disturbance sources was lowered to a minimum. In response to this finding, a new concept of spatial segmentation is devised in this research based on magnetic anomaly (MA). Therefore, this study focuses on developing innovative techniques based on spatial segmentation strategy and machine learning algorithms for effective indoor localisation using EMF. In this thesis, four closely correlated components in the proposed system are included: (i) Kriging interpolation-based fingerprinting map; (ii) magnetic intensity-based spatial segmentation; (iii) weighted Naïve Bayes classification (WNBC); (iv) fused features-based k-Nearest-Neighbours (kNN) algorithm. Kriging interpolation-based fingerprinting map reconstructs the original observed EMF positioning database in the calibration phase by interpolating predicted points. The magnetic intensity-based spatial segmentation component then investigates the variation tendency of ambient EMF signals in the new database to analyse the distribution of magnetic disturbance sources, and accordingly, segmenting the test site. Then, WNBC blends the exclusive characteristics of indoor EMF into original Naïve Bayes Classification (NBC) to enable a more accurate and efficient segmentation approach. It is well known that the best IPS implementation often exerts the use of multiple positing sources in order to maximise accuracy. The fused features-based kNN component used in the positioning phase finally learns the various parameters collected in the calibration phase, continuously improving the positioning accuracy of the system. The proposed system was evaluated on multiple indoor sites with diverse layouts. The results show that it outperforms state-of-the-art approaches and demonstrate an average accuracy between 1-2 meters achieved in typical sites by the best methods proposed in this thesis across most of the experimental environments. It can be believed that such an accurate approach will enable the future of infrastructure–free IPS technologies

Conceptualization of Computational Modeling Approaches and Interpretation of the Role of Neuroimaging Indices in Pathomechanisms for Pre-Clinical Detection of Alzheimer Disease

With swift advancements in next-generation sequencing technologies alongside the voluminous growth of biological data, a diversity of various data resources such as databases and web services have been created to facilitate data management, accessibility, and analysis. However, the burden of interoperability between dynamically growing data resources is an increasingly rate-limiting step in biomedicine, specifically concerning neurodegeneration. Over the years, massive investments and technological advancements for dementia research have resulted in large proportions of unmined data. Accordingly, there is an essential need for intelligent as well as integrative approaches to mine available data and substantiate novel research outcomes. Semantic frameworks provide a unique possibility to integrate multiple heterogeneous, high-resolution data resources with semantic integrity using standardized ontologies and vocabularies for context- specific domains. In this current work, (i) the functionality of a semantically structured terminology for mining pathway relevant knowledge from the literature, called Pathway Terminology System, is demonstrated and (ii) a context-specific high granularity semantic framework for neurodegenerative diseases, known as NeuroRDF, is presented. Neurodegenerative disorders are especially complex as they are characterized by widespread manifestations and the potential for dramatic alterations in disease progression over time. Early detection and prediction strategies through clinical pointers can provide promising solutions for effective treatment of AD. In the current work, we have presented the importance of bridging the gap between clinical and molecular biomarkers to effectively contribute to dementia research. Moreover, we address the need for a formalized framework called NIFT to automatically mine relevant clinical knowledge from the literature for substantiating high-resolution cause-and-effect models

Genome bioinformatics of tomato and potato

In the past two decades genome sequencing has developed from a laborious and costly technology employed by large international consortia to a widely used, automated and affordable tool used worldwide by many individual research groups. Genome sequences of many food animals and crop plants have been deciphered and are being exploited for fundamental research and applied to improve their breeding programs. The developments in sequencing technologies have also impacted the associated bioinformatics strategies and tools, both those that are required for data processing, management, and quality control, and those used for interpretation of the data. This thesis focuses on the application of genome sequencing, assembly and annotation to two members of the Solanaceae family, tomato and potato. Potato is the economically most important species within the Solanaceae, and its tubers contribute to dietary intake of starch, protein, antioxidants, and vitamins. Tomato fruits are the second most consumed vegetable after potato, and are a globally important dietary source of lycopene, beta-carotene, vitamin C, and fiber. The chapters in this thesis document the generation, exploitation and interpretation of genomic sequence resources for these two species and shed light on the contents, structure and evolution of their genomes. Chapter 1introduces the concepts of genome sequencing, assembly and annotation, and explains the novel genome sequencing technologies that have been developed in the past decade. These so-called Next Generation Sequencing platforms display considerable variation in chemistry and workflow, and as a consequence the throughput and data quality differs by orders of magnitude between the platforms. The currently available sequencing platforms produce a vast variety of read lengths and facilitate the generation of paired sequences with an approximately fixed distance between them. The choice of sequencing chemistry and platform combined with the type of sequencing template demands specifically adapted bioinformatics for data processing and interpretation. Irrespective of the sequencing and assembly strategy that is chosen, the resulting genome sequence, often represented by a collection of long linear strings of nucleotides, is of limited interest by itself. Interpretation of the genome can only be achieved through sequence annotation – that is, identification and classification of all functional elements in a genome sequence. Once these elements have been annotated, sequence alignments between multiple genomes of related accessions or species can be utilized to reveal the genetic variation on both the nucleotide and the structural level that underlies the difference between these species or accessions. Chapter 2describes BlastIf, a novel software tool that exploits sequence similarity searches with BLAST to provide a straightforward annotation of long nucleotide sequences. Generally, two problems are associated with the alignment of a long nucleotide sequence to a database of short gene or protein sequences: (i) the large number of similar hits that can be generated due to database redundancy; and (ii) the relationships implied between aligned segments within a hit that in fact correspond to distinct elements on the sequence such as genes. BlastIf generates a comprehensible BLAST output for long nucleotide sequences by reducing the number of similar hits while revealing most of the variation present between hits. It is a valuable tool for molecular biologists who wish to get a quick overview of the genetic elements present in a newly sequenced segment of DNA, prior to more elaborate efforts of gene structure prediction and annotation. In Chapter 3 a first genome-wide comparison between the emerging genomic sequence resources of tomato and potato is presented. Large collections of BAC end sequences from both species were annotated through repeat searches, transcript alignments and protein domain identification. In-depth comparisons of the annotated sequences revealed remarkable differences in both gene and repeat content between these closely related genomes. The tomato genome was found to be more repetitive than the potato genome, and substantial differences in the distribution of Gypsy and Copia retrotransposable elements as well as microsatellites were observed between the two genomes. A higher gene content was identified in the potato sequences, and in particular several large gene families including cytochrome P450 mono-oxygenases and serine-threonine protein kinases were significantly overrepresented in potato compared to tomato. Moreover, the cytochrome P450 gene family was found to be expanded in both tomato and potato when compared to Arabidopsis thaliana, suggesting an expanded network of secondary metabolic pathways in the Solanaceae. Together these findings present a first glimpse into the evolution of Solanaceous genomes, both within the family and relative to other plant species. Chapter 4explores the physical and genetic organization of tomato chromosome 6 through integration of BAC sequence analysis, High Information Content Fingerprinting, genetic analysis, and BAC-FISH mapping data. A collection of BACs spanning substantial parts of the short and long arm euchromatin and several dispersed regions of the pericentrometric heterochromatin were sequenced and assembled into several tiling paths spanning approximately 11 Mb. Overall, the cytogenetic order of BACs was in agreement with the order of BACs anchored to the Tomato EXPEN 2000 genetic map, although a few striking discrepancies were observed. The integration of BAC-FISH, sequence and genetic mapping data furthermore provided a clear picture of the borders between eu- and heterochromatin on chromosome 6. Annotation of the BAC sequences revealed that, although the majority of protein-coding genes were located in the euchromatin, the highly repetitive pericentromeric heterochromatin displayed an unexpectedly high gene content. Moreover, the short arm euchromatin was relatively rich in repeats, but the ratio of Gypsy and Copia retrotransposons across the different domains of the chromosome clearly distinguished euchromatin from heterochromatin. The ongoing whole-genome sequencing effort will reveal if these properties are unique for tomato chromosome 6, or a more general property of the tomato genome. Chapter 5presents the potato genome, the first genome sequence of an Asterid. To overcome the problems associated with genome assembly due tothe high level of heterozygosity that is observed in commercial tetraploid potato varieties, a homozygous doubled-monoploid potato clone was exploited to sequence and assemble 86% of the 844 Mb genome. This potato reference genome sequence was complemented with re-sequencing of aheterozygous diploid clone, revealing the form and extent of sequence polymorphism both between different genotypes and within a single heterozygous genotype. Gene presence/absence variants and other potentially deleterious mutations were found to occur frequently in potato and are a likely cause of inbreeding depression. Annotation of the genome was supported by deep transcriptome sequencing of both the doubled-monoploid and the heterozygous potato, resulting in the prediction of more than 39,000 protein coding genes. Transcriptome analysis provided evidence for the contribution of gene family expansion, tissue specific expression, and recruitment of genes to new pathways to the evolution of tuber development. The sequence of the potato genome has provided new insights into Eudicot genome evolution and has provided a solid basis for the elucidation of the evolution of tuberisation. Many traits of interest to plant breeders are quantitative in nature and the potato sequence will simplify both their characterization and deployment to generate novel cultivars. The outstanding challenges in plant genome sequencing are addressed in Chapter 6. The high concentration of repetitive elements and the heterozygosity and polyploidy of many interesting crop plant species currently pose a barrier for the efficient reconstruction of their genome sequences. Nonetheless, the completion of a large number of new genome sequences in recent years and the ongoing advances in sequencing technology provide many excitingopportunities for plant breeding and genome research. Current sequencing platforms are being continuously updated and improved, and novel technologies are being developed and implemented in third-generation sequencing platforms that sequence individual molecules without need for amplification. While these technologies create exciting opportunities for new sequencing applications, they also require robust software tools to process the data produced through them efficiently. The ever increasing amount of available genome sequences creates the need for an intuitive platform for the automated and reproducible interrogation of these data in order to formulate new biologically relevant questions on datasets spanning hundreds or thousands of genome sequences. </p

Simple identification tools in FishBase

Simple identification tools for fish species were included in the FishBase information system from its inception. Early tools made use of the relational model and characters like fin ray meristics. Soon pictures and drawings were added as a further help, similar to a field guide. Later came the computerization of existing dichotomous keys, again in combination with pictures and other information, and the ability to restrict possible species by country, area, or taxonomic group. Today, www.FishBase.org offers four different ways to identify species. This paper describes these tools with their advantages and disadvantages, and suggests various options for further development. It explores the possibility of a holistic and integrated computeraided strategy

Extending Critical Infrastructure Element Longevity using Constellation-based ID Verification

This work supports a technical cradle-to-grave protection strategy aimed at extending the useful lifespan of Critical Infrastructure (CI) elements. This is done by improving mid-life operational protection measures through integration of reliable physical (PHY) layer security mechanisms. The goal is to improve existing protection that is heavily reliant on higher-layer mechanisms that are commonly targeted by cyberattack. Relative to prior device ID discrimination works, results herein reinforce the exploitability of constellation-based PHY layer features and the ability for those features to be practically implemented to enhance CI security. Prior work is extended by formalizing a device ID verification process that enables rogue device detection demonstration under physical access attack conditions that include unauthorized devices mimicking bit-level credentials of authorized network devices. The work transitions from distance-based to probability-based measures of similarity derived from empirical Multivariate Normal Probability Density Function (MVNPDF) statistics of multiple discriminant analysis radio frequency fingerprint projections. Demonstration results for Constellation-Based Distinct Native Attribute (CB-DNA) fingerprinting of WirelessHART adapters from two manufacturers includes 1) average cross-class percent correct classification of %C \u3e 90% across 28 different networks comprised of six authorized devices, and 2) average rogue rejection rate of 83.4% ≤ RRR ≤ 99.9% based on two held-out devices serving as attacking rogue devices for each network (a total of 120 individual rogue attacks). Using the MVNPDF measure proved most effective and yielded nearly 12% RRR improvement over a Euclidean distance measure

Recent Application in Biometrics

In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers

Two-dimensional infrared spectroscopy for protein analysis

A number of forms of coherent multi-dimensional vibrational spectroscopy (CMDVS) have been identified as being useful for addressing a range of biological problems. Here a particular member of this family of spectroscopies, electronvibration- vibration two-dimensional infrared (EVV 2DIR) spectroscopy (also known as DOubly-Vibrationally Enhanced InfraRed (DOVE-IR)), is explored for its possible utility for two particular bioanalytical applications; protein identification and the study of enzyme mechanisms. The main focus of this work is on the development of EVV 2DIR as a tool for high-throughput, label-free proteomics, in particular for protein identification and absolute quantification. The protein fingerprinting strategy is based on the identification of proteins through their spectroscopically determined amino acid compositions. To this end, spectral signatures of amino acid side chains (tyrosine, phenylalanine and tryptophan) have been identified, as well as those from CH2 and CH3 groups which have been found to be appropriate for use as internal references. The intensities of these cross peaks are measured to give proteins’ amino acid compositions in the form of amino acid / CHx ratios. Specialised databases comprising the amino acid / CHx ratios of proteins have been developed for achieving protein identifications using the EVV 2DIR data. The second strand of this research considers the potential of triply resonant EVV 2DIR for studying protein structures and mechanisms. It is possible to use the electronic polarising properties of EVV 2DIR to good effect to achieve significant enhancement of the signal size when probing a chromophore. Here this effect is demonstrated for the case of bacteriorhodopsin (bR) membranes isolated from Halobacterium salinarium. The signal enhancement that is achievable from the retinal chromophore at the heart of bR makes it possible to study this whilst avoiding the surrounding protein