5,249 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
Explainable Spatio-Temporal Graph Neural Networks
Spatio-temporal graph neural networks (STGNNs) have gained popularity as a
powerful tool for effectively modeling spatio-temporal dependencies in diverse
real-world urban applications, including intelligent transportation and public
safety. However, the black-box nature of STGNNs limits their interpretability,
hindering their application in scenarios related to urban resource allocation
and policy formulation. To bridge this gap, we propose an Explainable
Spatio-Temporal Graph Neural Networks (STExplainer) framework that enhances
STGNNs with inherent explainability, enabling them to provide accurate
predictions and faithful explanations simultaneously. Our framework integrates
a unified spatio-temporal graph attention network with a positional information
fusion layer as the STG encoder and decoder, respectively. Furthermore, we
propose a structure distillation approach based on the Graph Information
Bottleneck (GIB) principle with an explainable objective, which is instantiated
by the STG encoder and decoder. Through extensive experiments, we demonstrate
that our STExplainer outperforms state-of-the-art baselines in terms of
predictive accuracy and explainability metrics (i.e., sparsity and fidelity) on
traffic and crime prediction tasks. Furthermore, our model exhibits superior
representation ability in alleviating data missing and sparsity issues. The
implementation code is available at: https://github.com/HKUDS/STExplainer.Comment: 32nd ACM International Conference on Information and Knowledge
Management (CIKM' 23
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