Energy-based temporal neural networks for imputing missing values

Imputing missing values in high dimensional time series is a difficult problem. There have been some approaches to the problem [11,8] where neural architectures were trained as probabilistic models of the data. However, we argue that this approach is not optimal. We propose to view temporal neural networks with latent variables as energy-based models and train them for missing value recovery directly. In this paper we introduce two energy-based models. The first model is based on a one dimensional convolution and the second model utilizes a recurrent neural network. We demonstrate how ideas from the energy-based learning framework can be used to train these models to recover missing values. The models are evaluated on a motion capture dataset

A Functional Taxonomy of Music Generation Systems

Digital advances have transformed the face of automatic music generation since its beginnings at the dawn of computing. Despite the many breakthroughs, issues such as the musical tasks targeted by different machines and the degree to which they succeed remain open questions. We present a functional taxonomy for music generation systems with reference to existing systems. The taxonomy organizes systems according to the purposes for which they were designed. It also reveals the inter-relatedness amongst the systems. This design-centered approach contrasts with predominant methods-based surveys and facilitates the identification of grand challenges to set the stage for new breakthroughs.Comment: survey, music generation, taxonomy, functional survey, survey, automatic composition, algorithmic compositio

Off-Policy Evaluation of Probabilistic Identity Data in Lookalike Modeling

We evaluate the impact of probabilistically-constructed digital identity data collected from Sep. to Dec. 2017 (approx.), in the context of Lookalike-targeted campaigns. The backbone of this study is a large set of probabilistically-constructed "identities", represented as small bags of cookies and mobile ad identifiers with associated metadata, that are likely all owned by the same underlying user. The identity data allows to generate "identity-based", rather than "identifier-based", user models, giving a fuller picture of the interests of the users underlying the identifiers. We employ off-policy techniques to evaluate the potential of identity-powered lookalike models without incurring the risk of allowing untested models to direct large amounts of ad spend or the large cost of performing A/B tests. We add to historical work on off-policy evaluation by noting a significant type of "finite-sample bias" that occurs for studies combining modestly-sized datasets and evaluation metrics involving rare events (e.g., conversions). We illustrate this bias using a simulation study that later informs the handling of inverse propensity weights in our analyses on real data. We demonstrate significant lift in identity-powered lookalikes versus an identity-ignorant baseline: on average ~70% lift in conversion rate. This rises to factors of ~(4-32)x for identifiers having little data themselves, but that can be inferred to belong to users with substantial data to aggregate across identifiers. This implies that identity-powered user modeling is especially important in the context of identifiers having very short lifespans (i.e., frequently churned cookies). Our work motivates and informs the use of probabilistically-constructed identities in marketing. It also deepens the canon of examples in which off-policy learning has been employed to evaluate the complex systems of the internet economy.Comment: Accepted by WSDM 201

AI Methods in Algorithmic Composition: A Comprehensive Survey

Algorithmic composition is the partial or total automation of the process of music composition by using computers. Since the 1950s, different computational techniques related to Artificial Intelligence have been used for algorithmic composition, including grammatical representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint programming and evolutionary algorithms. This survey aims to be a comprehensive account of research on algorithmic composition, presenting a thorough view of the field for researchers in Artificial Intelligence.This study was partially supported by a grant for the MELOMICS project (IPT-300000-2010-010) from the Spanish Ministerio de Ciencia e Innovación, and a grant for the CAUCE project (TSI-090302-2011-8) from the Spanish Ministerio de Industria, Turismo y Comercio. The first author was supported by a grant for the GENEX project (P09-TIC- 5123) from the Consejería de Innovación y Ciencia de Andalucía

Improving the Outcome of a Probabilistic Logic Music System Generator by Using Perlin Noise

APOPCALEAPS is a logic-based music generation program that uses high level probabilistic rules. The music produced by APOPCALEAPS is controlled by parameters that can be customized by a user to create personalized songs. Perlin noise is a type of gradient noise algorithm which generates smooth and controllable variations of random numbers. This paper introduces the idea of using a Perlin noise algorithm on songs produced by APOPCALEAPS to alter their melody. The noise system modifies the song’s melody with noise values that fluctuate as measures change in a song. Songs with more notes and more elaborate differences between the notes are modified by the system more than simpler songs. The output of the system is a different but similar song. This research can be used for generation of music with structure where one would need to generate variants on a theme

L-Music: uma abordagem para composição musical assistida usando L-Systems

Generative music systems have been researched for an extended period of time. The scientific corpus of this research field is translating, currently, into the world of the everyday musician and composer. With these tools, the creative process of writing music can be augmented or completely replaced by machines. The work in this document aims to contribute to research in assisted music composition systems. To do so, a review on the state of the art of these fields was performed and we found that a plethora of methodologies and approaches each provide their own interesting results (to name a few, neural networks, statistical models, and formal grammars). We identified Lindenmayer Systems, or L-Systems, as the most interesting and least explored approach to develop an assisted music composition system prototype, aptly named L-Music, due to the ability of producing complex outputs from simple structures. L-Systems were initially proposed as a parallel string rewriting grammar to model algae plant growth. Their applications soon turned graphical (e.g., drawing fractals), and eventually they were applied to music generation. Given that our prototype is assistive, we also took the user interface and user experience design into its well-deserved consideration. Our implemented interface is straightforward, simple to use with a structured visual hierarchy and flow and enables musicians and composers to select their desired instruments; select L-Systems for generating music or create their own custom ones and edit musical parameters (e.g., scale and octave range) to further control the outcome of L-Music, which is musical fragments that a musician or composer can then use in their own works. Three musical interpretations on L-Systems were implemented: a random interpretation, a scale-based interpretation, and a polyphonic interpretation. All three approaches produced interesting musical ideas, which we found to be potentially usable by musicians and composers in their own creative works. Although positive results were obtained, the developed prototype has many improvements for future work. Further musical interpretations can be added, as well as increasing the number of possible musical parameters that a user can edit. We also identified the possibility of giving the user control over what musical meaning L-Systems have as an interesting future challenge.Sistemas de geração de música têm sido alvo de investigação durante períodos alargados de tempo. Recentemente, tem havido esforços em passar o conhecimento adquirido de sistemas de geração de música autónomos e assistidos para as mãos do músico e compositor. Com estas ferramentas, o processo criativo pode ser enaltecido ou completamente substituído por máquinas. O presente trabalho visa contribuir para a investigação de sistemas de composição musical assistida. Para tal, foi efetuado um estudo do estado da arte destas temáticas, sendo que foram encontradas diversas metodologias que ofereciam resultados interessantes de um ponto de vista técnico e musical. Os sistemas de Lindenmayer, ou L-Systems, foram selecionados como a abordagem mais interessante, e menos explorada, para desenvolver um protótipo de um sistema de composição musical assistido com o nome L-Music, devido à sua capacidade de produzirem resultados complexos a partir de estruturas simples. Os L-Systems, inicialmente propostos para modelar o crescimento de plantas de algas, são gramáticas formais, cujo processo de reescrita de strings acontece de forma paralela. As suas aplicações rapidamente evoluíram para interpretações gráficas (p.e., desenhar fractais), e eventualmente também foram aplicados à geração de música. Dada a natureza assistida do protótipo desenvolvido, houve uma especial atenção dada ao design da interface e experiência do utilizador. Esta, é concisa e simples, tendo uma hierarquia visual estruturada para oferecer uma orientação coesa ao utilizador. Neste protótipo, os utilizadores podem selecionar instrumentos; selecionar L-Systems ou criar os seus próprios, e editar parâmetros musicais (p.e., escala e intervalo de oitavas) de forma a gerarem excertos musicais que possam usar nas suas próprias composições. Foram implementadas três interpretações musicais de L-Systems: uma interpretação aleatória, uma interpretação à base de escalas e uma interpretação polifónica. Todas as interpretações produziram resultados musicais interessantes, e provaram ter potencial para serem utilizadas por músicos e compositores nos seus trabalhos criativos. Embora tenham sido alcançados resultados positivos, o protótipo desenvolvido apresenta múltiplas melhorias para trabalho futuro. Entre elas estão, por exemplo, a adição de mais interpretações musicais e a adição de mais parâmetros musicais editáveis pelo utilizador. A possibilidade de um utilizador controlar o significado musical de um L-System também foi identificada como uma proposta futura relevante

Polyphonic music generation using neural networks

In this project, the application of generative models for polyphonic music generation is investigated. Polyphonic music generation falls into the field of algorithmic composition, which is a field that aims to develop models to automate, partially or completely, the composition of musical pieces. This process has many challenges both in terms of how to achieve the generation of musical pieces that are enjoyable and also how to perform a robust evaluation of the model to guide improvements. An extensive survey of the development of the field and the state-of-the-art is carried out. From this, two distinct generative models were chosen to apply to the problem of polyphonic music generation. The models chosen were the Restricted Boltzmann Machine and the Generative Adversarial Network. In particular, for the GAN, two architectures were used, the Deep Convolutional GAN and the Wasserstein GAN with gradient penalty. To train these models, a dataset containing over 9000 samples of classical musical pieces was used. Using a piano-roll representation of the musical pieces, these were converted into binary 2D arrays in which the vertical dimensions related to the pitch while the horizontal dimension represented the time, and note events were represented by active units. The first 16 seconds of each piece was extracted and used for training the model after applying data cleansing and preprocessing. Using implementations of these models, samples of musical pieces were generated. Based on listening tests performed by participants, the Deep Convolutional GAN achieved the best scores, with its compositions being ranked on average 4.80 on a scale from 1-5 of how enjoyable the pieces were. To perform a more objective evaluation, different musical features that describe rhythmic and melodic characteristics were extracted from the generated pieces and compared against the training dataset. These features included the implementation of the Krumhansl-Schmuckler algorithm for musical key detection and the average information rate used as an estimator of long-term musical structure. Within each set of the generated musical samples, the pairwise cross-validation using the Euclidean distance between each feature was performed. This was also performed between each set of generated samples and the features extracted from the training data, resulting in two sets of distances, the intra-set and inter-set distances. Using kernel density estimation, the probability density functions of these are obtained. Finally, the Kullback-Liebler divergence between the intra-set and inter-set distance of each feature for each generative model was calculated. The lower divergence indicates that the distributions are more similar. On average, the Restricted Boltzmann Machine obtained the lowest Kullback-Liebler divergences