A Convolutional Approach to Melody Line Identification in Symbolic Scores

In many musical traditions, the melody line is of primary significance in a piece. Human listeners can readily distinguish melodies from accompaniment; however, making this distinction given only the written score -- i.e. without listening to the music performed -- can be a difficult task. Solving this task is of great importance for both Music Information Retrieval and musicological applications. In this paper, we propose an automated approach to identifying the most salient melody line in a symbolic score. The backbone of the method consists of a convolutional neural network (CNN) estimating the probability that each note in the score (more precisely: each pixel in a piano roll encoding of the score) belongs to the melody line. We train and evaluate the method on various datasets, using manual annotations where available and solo instrument parts where not. We also propose a method to inspect the CNN and to analyze the influence exerted by notes on the prediction of other notes; this method can be applied whenever the output of a neural network has the same size as the input

Language of music: a computational model of music interpretation

Automatic music transcription (AMT) is commonly defined as the process of converting an acoustic musical signal into some form of musical notation, and can be split into two separate phases: (1) multi-pitch detection, the conversion of an audio signal into a time-frequency representation similar to a MIDI file; and (2) converting from this time-frequency representation into a musical score. A substantial amount of AMT research in recent years has concentrated on multi-pitch detection, and yet, in the case of the transcription of polyphonic music, there has been little progress. There are many potential reasons for this slow progress, but this thesis concentrates on the (lack of) use of music language models during the transcription process. In particular, a music language model would impart to a transcription system the background knowledge of music theory upon which a human transcriber relies. In the related field of automatic speech recognition, it has been shown that the use of a language model drawn from the field of natural language processing (NLP) is an essential component of a system for transcribing spoken word into text, and there is no reason to believe that music should be any different. This thesis will show that a music language model inspired by NLP techniques can be used successfully for transcription. In fact, this thesis will create the blueprint for such a music language model. We begin with a brief overview of existing multi-pitch detection systems, in particular noting four key properties which any music language model should have to be useful for integration into a joint system for AMT: it should (1) be probabilistic, (2) not use any data a priori, (3) be able to run on live performance data, and (4) be incremental. We then investigate voice separation, creating a model which achieves state-of-the-art performance on the task, and show that, used as a simple music language model, it improves multi-pitch detection performance significantly. This is followed by an investigation of metrical detection and alignment, where we introduce a grammar crafted for the task which, combined with a beat-tracking model, achieves state-of-the-art results on metrical alignment. This system’s success adds more evidence to the long-existing hypothesis that music and language consist of extremely similar structures. We end by investigating the joint analysis of music, in particular showing that a combination of our two models running jointly outperforms each running independently. We also introduce a new joint, automatic, quantitative metric for the complete transcription of an audio recording into an annotated musical score, something which the field currently lacks

Structuring lute tablature and MIDI data: Machine learning models for voice separation in symbolic music representations

This thesis concerns the design, development, and implementation of machine learning models for voice separation in two forms of symbolic music representations: lute tablature and MIDI data. Three modelling approaches are described: MA1, a note-level classification approach using a neural network, MA2, a chord-level regression approach using a neural network, and MA3, a chord-level probabilistic approach using a hidden Markov model. Furthermore, three model extensions are presented: backward processing, modelling voice and duration simultaneously, and multi-pass processing using an extended (bidirectional) decision context. Two datasets are created for model evaluation: a tablature dataset, containing a total of 15 three-voice and four-voice intabulations (lute arrangements of polyphonic vocal works) in a custom-made tablature encoding format, tab+, as well as in MIDI format, and a Bach dataset, containing the 45 three-voice and four-voice fugues from Johann Sebastian Bach’s _Das wohltemperirte Clavier_ (BWV 846-893) in MIDI format. The datasets are made available publicly, as is the software used to implement the models and the framework for training and evaluating them. The models are evaluated on the datasets in four experiments. The first experiment, where the different modelling approaches are compared, shows that MA1 is the most effective and efficient approach. The second experiment shows that the features are effective, and it demonstrates the importance of the type and amount of context information that is encoded in the feature vectors. The third experiment, which concerns model extension, shows that modelling backward and modelling voice and duration simultaneously do not lead to the hypothesised increase in model performance, but that using a multi-pass bidirectional model does. In the last experiment, where the performance of the models is compared with that of existing state-of-the-art systems for voice separation, it is shown that the models described in this thesis can compete with these systems

Platyrrhine Phylogenetics With A Focus On Callitrichine Life History Adaptations

The life history of a species is highly impacted by their reproductive strategy. In my dissertation I address the changing reproductive strategies in callitrichine New World monkeys and their genetic underpinnings using a phylogenetic approach. The necessity for a resolved phylogeny is universal to any comparative genomic study. Here we have constructed a reliable phylogenetic framework from which reproductive strategy could be studied in callitrichines. First, to determine the most recent common ancestor of Anthropoid primates we took a phylogenomic approach, using the publicly available whole genome sequences of 17 mammal species. With high confidence, we determined here that Tarsier is the most recent common ancestor to Anthropoid primates. Secondly, we construct a reliable phylogenetic framework for New World monkeys. To do this, genomic sequence databases are developed and parsed for non-genic markers. The resulting phylogeny is based on 40+kb of non-genic genomic data and contains 40 species. Finally the reproductive strategy of callitrichines was investigated. The timing and mechanism of litter size reduction in Goeldi\u27s monkey was accessed though detection of chimerism and adaptive evolution of genes involved in reproduction. We determined based on these analysis that the reduction in litter size is likely pre-ovulation and due to a reversion to mono-ovulation in the species

Uncovering Biological Meaning From Genome-Scale Datasets: A Challenge and an Opportunity

As DNA sequencing technologies continue to advance, resulting in increased throughput and decreased costs, both the number of researchers utilizing these technologies and the quantity of data outputted by a single sequencing experiment will, likewise, continue to increase. Currently, DNA sequence data can be generated at a much faster rate than computational tools can be created for the management, storage, and analysis of these large-scale datasets. While large genome-scale datasets have the capacity to fuel the next generation of scientific discovery, leveraging these datasets requires that researchers identify ways to uncover meaning from the data. Both performance of a research project in Dr. Dee Denver’s Laboratory and an internship experience with Intuitive Genomics, Inc provided me an opportunity for exploration of methods for uncovering meaning from genome-scale datasets. Intuitive Genomics, Inc. is a bioinformatics services startup located in St. Louis, Missouri. Serving as the company’s Marketing Manager, the ultimate goal of my internship project was significant contribution to the growth and success of the startup. Areas of responsibility included marketing, sales, strategic development of products and services, and administration. Key accomplishments included significant contribution of content to the company’s newly launched website, effective market research and competitor analysis, development of marketing materials, successful submission of an application for funding, professional interaction with current and potential customers, and representation of the company at a vendor show. The internship experience exposed me to a dynamic entrepreneurial environment, increased my knowledge of computational science, gave me first-hand experience with a variety of business concepts, enhanced my written and oral communication skills, and increased my confidence in professional interactions with customers and colleagues. These new skills and exposures will prepare me well for a career with a scientific corporation. Intuitive Genomics benefitted from an employee’s 100% dedication to the company. I served as an administrative contact, a presence in the office space, conducted a variety of pertinent research initiatives, developed meaningful content for the website, developed marketing materials and served as a point of contact for current and potential customers

THE GENOMIC DYNAMICS OF FERMENTED FOOD MICROBES

Along with the agriculture and domestication revolution, humans have utilized bacteria, yeasts, and molds for millennia in the production of traditionally fermented foods and beverages. Fermentation is a very ancient practice of high relevance nowadays since it contributes with a great variety of foods worldwide. Microbial fermentation allows metabolic transformation of the raw food materials leading to biochemical changes that played a key role in food preservation, health benefits, nutrition, flavors, and texture, among others. Food fermentation practices could diverge from traditional artisanal spontaneous fermentation to industrialize methods with specialized microbial starters and although fermented environments tend to be very stable compared to a wild environment, microbial dynamics are variable and fluctuating in terms of function diversity and abundance. Additionally, the process of backslopping, the constant transfer of previous fermented material to new batches could led to microbial specialization to human-made environment causing microbes to undergo domestication where adapted lineages are genetically differentiated from wild species. The goal of my dissertation was to understand and evaluate genomic and functional changes in fermenting food microbes and understand the impact of selection in domesticated microbes

The Computational Analysis of Harmony in Western Art Music.

PhDThis thesis describes research in the computational analysis of harmony in western art music, focussing particularly on improving the accuracy and information-richness of key and chord extraction from digital score data. It is argued that a greater sophistication in automatic harmony analysis is an important contribution to the field of computational musicology. Initial experiments use hidden Markov models to predict key and modulation from automatically labelled chord sequences. Model parameters are based on heuristically formulated chord and key weightings derived from Sch¨onberg’s harmonic theory and the key and chord ratings resulting from perceptual experiments with listeners. The music theory models are shown to outperform the perceptual models both in terms of key accuracy and modelling the precise moment of key change. All of the models perform well enough to generate descriptive data about modulatory frequency, modulatory type and key distance. A robust method of classifying underlying chord types from elaborated keyboard music is then detailed. The method successfully distinguishes between essential and inessential notes, for example, passing notes and neighbour notes, and combines note classification information with tertian chord potential to measure the harmonic importance of a note. Existing approaches to automatic chord classification are unsuitable for use with complex textures and are restricted to triads and simple sevenths. An important goal is therefore to recognise a much broader set of chords, including complex chord types such as 9ths, 11ths and 13ths. This level of detail is necessary if the methods are to supply sophisticated information about the harmonic techniques of composers. Testing on the first twenty-four preludes of J. S. Bach’s Well Tempered Clavier, hand annotated by the author, a state of the art approach achieves 22.1% accuracy; our method achieves 55% accuracy.Engineering and Physical Sciences Research Council (EPSRC) DTA studentship

Disentangling hexaploid genetics : towards DNA-informed breeding for postharvest performance in chrysanthemum

DNA-informed selection can strongly improve the process of plant breeding. It requires the detection of DNA polymorphisms, calculation of genetic linkage, access to reliable phenotypes and methods to detect genetic loci associated with phenotypic traits of interest. Cultivated chrysanthemum is an outcrossing hexaploid with an unknown mode of inheritance. This complicates the development of resources and methods that enable the detection of trait loci. Postharvest performance is an essential trait in chrysanthemum, but is difficult to measure. This makes it an interesting but challenging trait to phenotype and detect associated genetic loci. In this thesis I describe the development of resources and methods to enable phenotyping for postharvest performance, genetic linkage map construction and detection of quantitative trait loci in hexaploid chrysanthemum. Postharvest performance is a complicated trait because it is related to many different disorders that reduce quality. One of these disorders in chrysanthemum is disk floret degreening, which occurs after long storage. In chapter 2, we show that degreening can be prevented by feeding the flower heads with sucrose, suggesting carbohydrate starvation plays a role in the degreening process. To investigate the response to carbohydrate starvation of genotypes with different sensitivity to disk floret degreening, we investigated the metabolome of sugar-fed and carbohydrate-starved disk florets by 1H-NMR and HPAEC. We show that the metabolome is severely altered at carbohydrate starvation. In general, starvation results in an upregulation of amino acid and secondary metabolism. Underlying causes of genotypic differences explaining variation in disk floret degreening in the three investigated genotypes remained to be elucidated, but roles of regulation of respiration rate and camphor metabolism were posed as possible candidates. In chapter 3, disk floret degreening was found to be the most important postharvest disorder after 3 weeks of storage among 44 white chrysanthemum cultivars. To investigate the inheritance of disk floret degreening, we crossed two genotypes with opposite phenotypic values of both disk floret degreening and carbohydrate content to obtain a population segregating for disk floret degreening. To phenotype the cultivar panel and the bi-parental population precisely and in a high throughput manner, we developed a method that quantified colour of detached capitula over time. This method was validated with visual observations of disk floret degreening during vase life tests. In a subset of the bi-parental population we measured carbohydrate content of the disk florets at harvest. The amount of total carbohydrates co-segregated with sensitivity to degreening, which shows that the difference in disk floret degreening sensitivity between the parents could be explained by their difference in carbohydrate content. However, the correlation was rather weak, indicating carbohydrate content is not the only factor playing a role. In order to develop resources for DNA-informed breeding, one needs to be able to characterize DNA polymorphisms. In chapter 4, we describe the development of a genotyping array containing 183,000 single nucleotide polymorphisms (SNPs). These SNPs were acquired by sequencing the transcriptome of 13 chrysanthemum cultivars. By comparing the genomic dosage based on the SNP assay and the dosage as estimated by the read depth from the transcriptome sequencing data, we show that alleles are expressed conform the genomic dosage, which contradicts to what is often found in disomic polyploids. In line with this finding, we conclusively show that cultivated chrysanthemum exhibits genome-wide hexasomic inheritance, based on the segregation ratios of large numbers of different types of markers in two different populations. Tools for genetic analysis in diploids are widely available, but these have limited use for polyploids. In chapter 5, we present a modular software package that enables genetic linkage map construction in tetraploids and hexaploids. Because of the modularity, functionality for other ploidy levels can be easily added. The software is written in the programming language R and we named it polymapR. It can generate genetic linkage maps from marker dosage scores in an F1 population, while taking the following steps: data inspection and filtering, linkage analysis, linkage group assignment and marker ordering. It is the first software package that can handle polysomic hexaploid and partial polysomic tetraploid data, and has advantages over other polyploid mapping software because of its scalability and cross-platform applicability. With the marker dosage scores of the bi-parental F1 population from the genotyping array and the developed methods to perform linkage analysis we constructed an integrated genetic linkage map for the hexaploid bi-parental population described in chapter 3 and 4. We describe this process in chapter 6. With this integrated linkage map, we reconstructed the inheritance of parental haplotypes for each individual, and expressed this as identity-by-descent (IBD) probabilities. The phenotypic data on disk floret degreening sensitivity that was acquired as described in chapter 3, was used in addition to three other traits to detect quantitative trait loci (QTL). These QTL were detected based on the IBD probabilities of 1 centiMorgan intervals of each parental homologue. This enabled us to study genetic architecture by estimating the effects of each separate allele within a QTL on the trait. We showed that for many QTL the trait was affected by more than two alleles. In chapter 7, the findings in this thesis are discussed in the context of breeding for heterogeneous traits, the implications of the mode of inheritance for breeding and the advantages and disadvantages of polyploidy in crop breeding. In conclusion, this thesis provides in general a significant step for DNA-informed breeding in polysomic hexaploids, and for postharvest performance in chrysanthemum in particular.</p