Deep neural networks with voice entry estimation heuristics for voice separation in symbolic music representations

In this study we explore the use of deep feedforward neural networks for voice separation in symbolic music representations. We experiment with different network architectures, varying the number and size of the hidden layers, and with dropout. We integrate two voice entry estimation heuristics that estimate the entry points of the individual voices in the polyphonic fabric into the models. These heuristics serve to reduce error propagation at the beginning of a piece, which, as we have shown in previous work, can seriously hamper model performance. The models are evaluated on the 48 fugues from Johann Sebastian Bach’s The Well-Tempered Clavier and his 30 inventions—a dataset that we curated and make publicly available. We find that a model with two hidden layers yields the best results. Using more layers does not lead to a significant performance improvement. Furthermore, we find that our voice entry estimation heuristics are highly effective in the reduction of error propagation, improving performance significantly. Our best-performing model outperforms our previous models, where the difference is significant, and, depending on the evaluation metric, performs close to or better than the reported state of the art

A machine learning approach to voice separation in lute tablature

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Bringing 'Musicque into the tableture': machine-learning models for polyphonic transcription of 16th-century lute tablature

A large corpus of music written in lute tablature, spanning some three-and-a-half centuries, has survived. This music has so far escaped systematic musicological research because of its notational format. Being a practical instruction for the player, tablature reveals very little of the polyphonic structure of the music it encodes—and is therefore relatively inaccessible to non-specialists. Automatic polyphonic transcription into modern music notation can help unlock the corpus to a larger audience, and thus facilitate musicological research. In this study we present four variants of a machine-learning model for voice separation and duration reconstruction in 16th-century lute tablature. These models are intended to form the heart of an interactive system for automatic polyphonic transcription that can assist users in making editions tailored to their own preferences. Additionally, such models can provide new methods for analysing different aspects of polyphonic structure. We have experimented with modelling only voice and modelling voice and duration simultaneously, applying each in a forward- and in a backward-processing approach. The models are evaluated on a dataset containing 15 three- and four-voice intabulations. Each processing approach has its advantages, and the results vary between the models. With accuracy rates between approximately 80 and 90 per cent, both for voice prediction and for duration prediction, the best models’ performance is promising. Even in this early stage of the research, such models yield a useful initial transcription system

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

Socionic Multi-Agent Systems Based on Reflexive Petri Nets and Theories of Social Self-Organisation

This contribution summarises the core results of the transdisciplinary ASKO project, part of the German DFG's programme Sozionik, which combines sociologists' and computer scientists' skills in order to create improved theories and models of artificial societies. Our research group has (a) formulated a social theory, which is able to explain fundamental mechanisms of self-organisation in both natural and artificial societies, (b) modelled this in a mathematical way using a visual formalism, and (c) developed a novel multi-agent system architecture which is conceptually coherent, recursively structured (hence non-eclectic) and based on our social theory. The article presents an outline of both a sociological middle-range theory of social self-organisation in educational institutions, its formal, Petri net based model, including a simulation of one of its main mechanisms, and the multi-agent system architecture SONAR. It describes how the theory was created by a re-analysis of some grand social theories, by grounding it empirically, and finally how the theory was evaluated by modelling its concepts and statements.Multi-Agents Systems, Petri Nets, Self-Organisation, Social Theories

High Resolution Genotyping of Clinical Aspergillus flavus Isolates from India Using Microsatellites

Contains fulltext : 124312.pdf (publisher's version ) (Open Access)BACKGROUND: Worldwide, Aspergillus flavus is the second leading cause of allergic, invasive and colonizing fungal diseases in humans. However, it is the most common species causing fungal rhinosinusitis and eye infections in tropical countries. Despite the growing challenges due to A. flavus, the molecular epidemiology of this fungus has not been well studied. We evaluated the use of microsatellites for high resolution genotyping of A. flavus from India and a possible connection between clinical presentation and genotype of the involved isolate. METHODOLOGY/PRINCIPAL FINDINGS: A panel of nine microsatellite markers were selected from the genome of A. flavus NRRL 3357. These markers were used to type 162 clinical isolates of A. flavus. All nine markers proved to be polymorphic displaying up to 33 alleles per marker. Thirteen isolates proved to be a mixture of different genotypes. Among the 149 pure isolates, 124 different genotypes could be recognized. The discriminatory power (D) for the individual markers ranged from 0.657 to 0.954. The D value of the panel of nine markers combined was 0.997. The multiplex multicolor approach was instrumental in rapid typing of a large number of isolates. There was no correlation between genotype and the clinical presentation of the infection. CONCLUSIONS/SIGNIFICANCE: There is a large genotypic diversity in clinical A. flavus isolates from India. The presence of more than one genotype in clinical samples illustrates the possibility that persons may be colonized by multiple genotypes and that any isolate from a clinical specimen is not necessarily the one actually causing infection. Microsatellites are excellent typing targets for discriminating between A. flavus isolates from various origins