6,615 research outputs found
VGM-RNN: Recurrent Neural Networks for Video Game Music Generation
The recent explosion of interest in deep neural networks has affected and in some cases reinvigorated work in fields as diverse as natural language processing, image recognition, speech recognition and many more. For sequence learning tasks, recurrent neural networks and in particular LSTM-based networks have shown promising results. Recently there has been interest – for example in the research by Google’s Magenta team – in applying so-called “language modeling” recurrent neural networks to musical tasks, including for the automatic generation of original music. In this work we demonstrate our own LSTM-based music language modeling recurrent network. We show that it is able to learn musical features from a MIDI dataset and generate output that is musically interesting while demonstrating features of melody, harmony and rhythm. We source our dataset from VGMusic.com, a collection of user-submitted MIDI transcriptions of video game songs, and attempt to generate output which emulates this kind of music
A Cross-Repository Model for Predicting Popularity in GitHub
Social coding platforms, such as GitHub, can serve as natural laboratories
for studying the diffusion of innovation through tracking the pattern of code
adoption by programmers. This paper focuses on the problem of predicting the
popularity of software repositories over time; our aim is to forecast the time
series of popularity-related events (code forks and watches). In particular, we
are interested in cross-repository patterns-how do events on one repository
affect other repositories? Our proposed LSTM (Long Short-Term Memory) recurrent
neural network integrates events across multiple active repositories,
outperforming a standard ARIMA (Auto-Regressive Integrated Moving Average) time
series prediction based on the single repository. The ability of the LSTM to
leverage cross-repository information gives it a significant edge over standard
time series forecasting.Comment: 6 page
A Neural Model for Generating Natural Language Summaries of Program Subroutines
Source code summarization -- creating natural language descriptions of source
code behavior -- is a rapidly-growing research topic with applications to
automatic documentation generation, program comprehension, and software
maintenance. Traditional techniques relied on heuristics and templates built
manually by human experts. Recently, data-driven approaches based on neural
machine translation have largely overtaken template-based systems. But nearly
all of these techniques rely almost entirely on programs having good internal
documentation; without clear identifier names, the models fail to create good
summaries. In this paper, we present a neural model that combines words from
code with code structure from an AST. Unlike previous approaches, our model
processes each data source as a separate input, which allows the model to learn
code structure independent of the text in code. This process helps our approach
provide coherent summaries in many cases even when zero internal documentation
is provided. We evaluate our technique with a dataset we created from 2.1m Java
methods. We find improvement over two baseline techniques from SE literature
and one from NLP literature
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