30,246 research outputs found
Music Generation by Deep Learning - Challenges and Directions
In addition to traditional tasks such as prediction, classification and
translation, deep learning is receiving growing attention as an approach for
music generation, as witnessed by recent research groups such as Magenta at
Google and CTRL (Creator Technology Research Lab) at Spotify. The motivation is
in using the capacity of deep learning architectures and training techniques to
automatically learn musical styles from arbitrary musical corpora and then to
generate samples from the estimated distribution. However, a direct application
of deep learning to generate content rapidly reaches limits as the generated
content tends to mimic the training set without exhibiting true creativity.
Moreover, deep learning architectures do not offer direct ways for controlling
generation (e.g., imposing some tonality or other arbitrary constraints).
Furthermore, deep learning architectures alone are autistic automata which
generate music autonomously without human user interaction, far from the
objective of interactively assisting musicians to compose and refine music.
Issues such as: control, structure, creativity and interactivity are the focus
of our analysis. In this paper, we select some limitations of a direct
application of deep learning to music generation, analyze why the issues are
not fulfilled and how to address them by possible approaches. Various examples
of recent systems are cited as examples of promising directions.Comment: 17 pages. arXiv admin note: substantial text overlap with
arXiv:1709.01620. Accepted for publication in Special Issue on Deep learning
for music and audio, Neural Computing & Applications, Springer Nature, 201
Learning by Seeing by Doing: Arithmetic Word Problems
Learning by doing in pursuit of real-world goals has received much attention from education researchers but has been unevenly supported by mathematics education software at the elementary level, particularly as it involves arithmetic word problems. In this article, we give examples of doing-oriented tools that might promote children\u27s ability to see significant abstract structures in mathematical situations. The reflection necessary for such seeing is motivated by activities and contexts that emphasize affective and social aspects. Natural language, as a representation already familiar to children, is key in these activities, both as a means of mathematical expression and as a link between situations and various abstract representations. These tools support children\u27s ownership of a mathematical problem and its expression; remote sharing of problems and data; software interpretation of children\u27s own word problems; play with dynamically linked representations with attention to children\u27s prior connections; and systematic problem variation based on empirically determined level of difficulty
A Case Study of the Impact of Musical Pattern Rehearsal on the Acquisition of Oral and Written Language Skills in a Young Child with Learning Differences
The study explores the relationship between learning musical patterns and learning language patterns. A case study of a male diagnosed with learning differences in generative writing and graphic processing indicates a positive relationship between the neurological patterning of rehearsed musical patterns and the acquisition of oral and written language skills. The anecdotal study tracks the development of literacy from the initial identification of dysfunctional patterns of performance at age three through the acquisition of oral language and the mastery of basic reading skills in the primary years. Analysis of the case study supports the need for musical training in the preschool setting as a foundational component of early literacy programs
Multiscale Markov Decision Problems: Compression, Solution, and Transfer Learning
Many problems in sequential decision making and stochastic control often have
natural multiscale structure: sub-tasks are assembled together to accomplish
complex goals. Systematically inferring and leveraging hierarchical structure,
particularly beyond a single level of abstraction, has remained a longstanding
challenge. We describe a fast multiscale procedure for repeatedly compressing,
or homogenizing, Markov decision processes (MDPs), wherein a hierarchy of
sub-problems at different scales is automatically determined. Coarsened MDPs
are themselves independent, deterministic MDPs, and may be solved using
existing algorithms. The multiscale representation delivered by this procedure
decouples sub-tasks from each other and can lead to substantial improvements in
convergence rates both locally within sub-problems and globally across
sub-problems, yielding significant computational savings. A second fundamental
aspect of this work is that these multiscale decompositions yield new transfer
opportunities across different problems, where solutions of sub-tasks at
different levels of the hierarchy may be amenable to transfer to new problems.
Localized transfer of policies and potential operators at arbitrary scales is
emphasized. Finally, we demonstrate compression and transfer in a collection of
illustrative domains, including examples involving discrete and continuous
statespaces.Comment: 86 pages, 15 figure
Exploration of Reaction Pathways and Chemical Transformation Networks
For the investigation of chemical reaction networks, the identification of
all relevant intermediates and elementary reactions is mandatory. Many
algorithmic approaches exist that perform explorations efficiently and
automatedly. These approaches differ in their application range, the level of
completeness of the exploration, as well as the amount of heuristics and human
intervention required. Here, we describe and compare the different approaches
based on these criteria. Future directions leveraging the strengths of chemical
heuristics, human interaction, and physical rigor are discussed.Comment: 48 pages, 4 figure
Representation of Samba dance gestures, using a multi-modal analysis approach
In this paper we propose an approach for the
representation of dance gestures in Samba dance. This
representation is based on a video analysis of body
movements, carried out from the viewpoint of the
musical meter. Our method provides the periods, a
measure of energy and a visual representation of
periodic movement in dance. The method is applied to
a limited universe of Samba dances and music, which
is used to illustrate the usefulness of the approach
- …