Title: DLR : Toward a deep learned rhythmic representation for music content analysis,
Abstract: In the use of deep neural networks, it is crucial to provide appropriate
input representations for the network to learn from. In this paper, we propose
an approach to learn a representation that focus on rhythmic representation
which is named as DLR (Deep Learning Rhythmic representation). The proposed
approach aims to learn DLR from the raw audio signal and use it for other music
informatics tasks. A 1-dimensional convolutional network is utilised in the
learning of DLR. In the experiment, we present the results from the source task
and the target task as well as visualisations of DLRs. The results reveals that
DLR provides compact rhythmic information which can be used on multi-tagging
task.