Algorithmic Composition of Melodies with Deep Recurrent Neural Networks

A big challenge in algorithmic composition is to devise a model that is both easily trainable and able to reproduce the long-range temporal dependencies typical of music. Here we investigate how artificial neural networks can be trained on a large corpus of melodies and turned into automated music composers able to generate new melodies coherent with the style they have been trained on. We employ gated recurrent unit networks that have been shown to be particularly efficient in learning complex sequential activations with arbitrary long time lags. Our model processes rhythm and melody in parallel while modeling the relation between these two features. Using such an approach, we were able to generate interesting complete melodies or suggest possible continuations of a melody fragment that is coherent with the characteristics of the fragment itself

Algorithmic composition of melodies with deep recurrent neural networks

A big challenge in algorithmic composition is to devise a model that is both easily trainable and able to reproduce the long-range temporal dependencies typical of music. Here we investigate how artificial neural networks can be trained on a large corpus of melodies and turned into automated mu- sic composers able to generate new melodies coherent with the style they have been trained on. We employ gated-recurrent unit (GRU) networks that have been shown to be particularly efficient in learning complex sequential activations with arbitrary long time lags. Our model processes rhythm and melody in parallel while modeling the relation between these two properties. Using such an approach, we were able to generate interesting complete melodies or suggest possible continuations of a melody fragment that is coherent with the characteristics of the fragment itself

Towards a Deep Improviser: a prototype deep learning post-tonal free music generator

Two modest-sized symbolic corpora of post-tonal and post-metrical keyboard music have been constructed, one algorithmic, the other improvised. Deep learning models of each have been trained. The purpose was to obtain models with sufficient generalisation capacity that in response to separate fresh input seed material, they can generate outputs that are statistically distinctive, neither random nor recreative of the learned corpora or the seed material. This objective has been achieved, as judged by k-sample Anderson-Darling and Cramer tests. Music has been generated using the approach, and preliminary informal judgements place it roughly on a par with an example of composed music in a related form. Future work will aim to enhance the model such that it deserves to be fully evaluated in relation to expression, meaning and utility in real-time performance

Composição automática de músicas utilizando redes neurais recorrentes

O problema de composição musical automática é extensivamente explorado na literatura. Em geral, o objetivo final nesses trabalhos é a composição musical em si, de forma que os modelos utilizados são ajustados para que a música gerada seja adequada em algum sentido. Detalhes técnicos como os impactos que as modificações nos parâmetros têm na composição final são amplamente desconhecidos. Neste trabalho, temos por objetivo estudar o quão sensível é um modelo de rede neural recorrente, baseado em processamento de linguagem natural, construído para composição musical. Para tal tarefa, a mensuração será feita com a perplexidade, uma medida oriunda da teoria da informação. Por fim, as músicas são avaliadas, de forma subjetiva, em relação à musicalidade e à qualidade das peças musicais obtidas.The issue of automatically composed music has received a lot of attention in the literature. In general, the focus is on the composition itself, and models are tuned to produce useful results. Technical details such as the effects that adjustments to the model’s parameters have on the composition, however, are still largely unknown. In this work, we investigate how sensitive is a recurrent neural network model, based on natural language processing, built for music composition. To accomplish this task, we apply a metric from information theory called perplexity. The composed works are then subjectively assessed for musicality and quality

Desenvolvimento de base de dados de imagens, classes e mensuração de úlceras do pé diabético para técnicas de classificação e ferramentas de auxílio a diagnóstico

Dissertação (Mestrado em Engenharia Biomédica)–Programa de pós-graduação em Engenharia Biomédica, Universidade de Brasília, Brasília, 2020.As Úlceras do Pé Diabético (UPDs) estão entre as principais e mais recorrentes complicações do Diabetes Mellitus (DM) na atualidade. Há uma vasta diversidade de tratamentos para as feridas diabéticas, que são consideradas feridas crônicas e de difícil cicatrização. A maior parte dos tratamentos resumem-se em pomadas, coberturas especiais e limpeza semanal realizadas nos ambulatórios. Esses tratamentos demandam longo prazo para a cicatrização de pequenas lesões e nem sempre têm efeito positivo de diminuição da ferida. O tratamento a base de lâminas de látex associadas a fototerapia é uma inovação para cicatrização dessas úlceras em menos tempo e melhor qualidade do tecido regenerado. As feridas diabéticas possuem diferentes estágios, para direcionar o tratamento adequado é de extrema importância a avaliação precisa da lesão. Por este motivo existem diversas escalas de classificação renomadas utilizadas como referência pelos profissionais de saúde. A classificação correta das úlceras é uma dificuldade enfrentada diariamente, pois cada profissional avalia de uma maneira própria. A partir dessa dificuldade dos profissionais, dos pacientes e tendo ciência da relevância de conjuntos de dados robustos, esta pesquisa busca desenvolver uma base de dados sólida com informações de UPDs. Na literatura científica existe carência de dados referentes a úlceras diabéticas, o que dificulta estudos da lesão e automatização de procedimentos repetitivos no ambiente hospitalar. Para que a base de dados atenda aos critérios estabelecidos, um gabarito para medições invasivas foi adaptado e utilizado a cada visita ambulatorial. A mensuração das úlceras diabéticas é um fator que estima a evolução da ferida, é capaz de direcionar o tipo de tratamento adequado e enfatiza o uso de sistemas metrológicos na saúde. O gabarito manuseado neste procedimento recolhe informações da lesão que são de extrema importância, como: Classificação da Universidade do Texas, comprimento, largura, tipo de exsudato, bordas da ferida e outras. Estas avaliações das feridas foram realizadas semanalmente por enfermeiros de dois Ambulatórios de Pé Diabético, que também auxiliaram na mensuração e registros fotográficos. Além das informações de avaliação da lesão organizadas em tabelas, as imagens digitais originais e outra parte segmentada manualmente compõem a base de dados. Este conjunto de informações da lesão possibilitará diversas pesquisas que buscam automatizar a classificação em ambientes de saúde por meio de Aprendizado de Máquina (AM) e estudos de processamento de imagens. O gabarito adaptado foi testado primeiramente em manequim para padronizar o processo nos pacientes. Em seguida os participantes da coleta foram triados nos hospitais e submetidos a seus respectivos tratamentos. Os elementos das feridas nos pacientes do ensaio clínico Rapha® e tratamento SUS foram coletados simultaneamente. Ao fim das coletas realizamos uma análise detalhada da quantidade de informação levantada sobre as feridas, bem como uma análise quantitativa da evolução das medidas ao longo do tratamento. Estas análises permitem observar tanto o progresso proporcionado pelo equipamento Rapha® no que diz respeito à metrologia das feridas, como a avaliação do aspecto de robustez da base de dados construída.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).Diabetic Foot Ulcers (DFU) are among the main and most recurring complications of Diabetes Mellitus (DM) today. There is a wide variety of treatments for diabetic wounds, which are considered chronic wounds and difficult to heal. Most treatments are summarized in ointments, special coverings and weekly cleaning performed in outpatient clinics. These treatments require long-term healing of small lesions and do not always have a positive effect on reducing the wound. The treatment based on latex sheets associated with phototherapy is an innovation for healing these ulcers in less time and better quality of regenerated tissue. Diabetic wounds have different stages, in order to target the appropriate treatment it is extremely important to accurately assess the injury. For this reason, there are several renowned classification scales used as a reference by health professionals. The correct classification of ulcers is a difficulty faced daily, as each professional evaluates in his own way. Based on this difficulty of professionals, patients and being aware of the relevance of robust data sets, this research seeks to develop a solid database with information from DFU. There is a lack of data in the scientific literature regarding diabetic ulcers, which makes it difficult to study the injury and automate repetitive procedures in the hospital environment. In order for the database to meet the established criteria, a template for invasive measurements was adapted and used for each outpatient visit. The measurement of diabetic ulcers is a factor that estimates the evolution of the wound, is able to direct the appropriate type of treatment and emphasizes the use of metrological systems in health. The template handled in this procedure collects information about the injury that is extremely important, such as: University of Texas Wound Classification System, length, width, type of exudate, wound edges and others. These wound assessments were carried out weekly by nurses from two Diabetic Foot Clinics, who also helped with measurement and photographic records. In addition to the injury assessment information organized in tables, the original digital images and another manually segmented part make up the database. This set of injury information will enable several researches that seek to automate the classification in healthcare environments through Machine Learning (ML) and image processing studies. The adapted template was first tested on a mannequin to standardize the process in patients. Then, the participants in the collection were screened in hospitals and submitted to their respective treatments. Wound elements in patients in the Rapha® clinical trial and Brazil's Unified Public Health System treatment were collected simultaneously. At the end of the collections, we performed a detailed analysis of the amount of information collected about the wounds, as well as a quantitative analysis of the evolution of the measures along the treatment. These analyzes allow observing both the progress provided by the Rapha® equipment with regard to the metrology of the wounds, as well as the assessment of the robustness aspect of the constructed database

Slow dynamics in structured neural network models

Humans and some other animals are able to perform tasks that require coordination of movements across multiple temporal scales, ranging from hundreds of milliseconds to several seconds. The fast timescale at which neurons naturally operate, on the order of tens of milliseconds, is well-suited to support motor control of rapid movements. In contrast, to coordinate movements on the order of seconds, a neural network should produce reliable dynamics on a similarly âslowâ timescale. Neurons and synapses exhibit biophysical mechanisms whose timescales range from tens of milliseconds to hours, which suggests a possible role of these mechanisms in producing slow reliable dynamics. However, how such mechanisms influence network dynamics is not yet understood. An alternative approach to achieve slow dynamics in a neural network consists in modifying its connectivity structure. Still, the limitations of this approach and in particular to what degree the weights require fine-tuning, remain unclear. Understanding how both the single neuron mechanisms and the connectivity structure might influence the network dynamics to produce slow timescales is the main goal of this thesis. We first consider the possibility of obtaining slow dynamics in binary networks by tuning their connectivity. It is known that binary networks can produce sequential dynamics. However, if the sequences consist of random patterns, the typical length of the longest sequence that can be produced grows linearly with the number of units. Here, we show that we can overcome this limitation by carefully designing the sequence structure. More precisely, we obtain a constructive proof that allows to obtain sequences whose length scales exponentially with the number of units. To achieve this however, one needs to exponentially fine-tune the connectivity matrix. Next, we focus on the interaction between single neuron mechanisms and recurrent dynamics. Particular attention is dedicated to adaptation, which is known to have a broad range of timescales and is therefore particularly interesting for the subject of this thesis. We study the dynamics of a random network with adaptation using mean-field techniques, and we show that the network can enter a state of resonant chaos. Interestingly, the resonance frequency of this state is independent of the connectivity strength and depends only on the properties of the single neuron model. The approach used to study networks with adaptation can also be applied when considering linear rate units with an arbitrary number of auxiliary variables. Based on a qualitative analysis of the mean-field theory for a random network whose neurons are described by a D -dimensional rate model, we conclude that the statistics of the chaotic dynamics are strongly influenced by the single neuron model under investigation. Using a reservoir computing approach, we show preliminary evidence that slow adaptation can be beneficial when performing tasks that require slow timescales. The positive impact of adaptation on the network performance is particularly strong in the presence of noise. Finally, we propose a network architecture in which the slowing-down effect due to adaptation is combined with a hierarchical structure, with the purpose of efficiently generate sequences that require multiple, hierarchically organized timescales