Deep Neural Networks for Music Tagging
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In this thesis, I present my hypothesis, experiment results, and discussion that are related
to various aspects of deep neural networks for music tagging.
Music tagging is a task to automatically predict the suitable semantic label when music is
provided. Generally speaking, the input of music tagging systems can be any entity that
constitutes music, e.g., audio content, lyrics, or metadata, but only the audio content
is considered in this thesis. My hypothesis is that we can fi nd effective deep learning
practices for the task of music tagging task that improves the classi fication performance.
As a computational model to realise a music tagging system, I use deep neural networks.
Combined with the research problem, the scope of this thesis is the understanding,
interpretation, optimisation, and application of deep neural networks in the context of
music tagging systems.
The ultimate goal of this thesis is to provide insight that can help to improve deep
learning-based music tagging systems. There are many smaller goals in this regard.
Since using deep neural networks is a data-driven approach, it is crucial to understand the
dataset. Selecting and designing a better architecture is the next topic to discuss. Since
the tagging is done with audio input, preprocessing the audio signal becomes one of the
important research topics. After building (or training) a music tagging system, fi nding
a suitable way to re-use it for other music information retrieval tasks is a compelling
topic, in addition to interpreting the trained system.
The evidence presented in the thesis supports that deep neural networks are powerful
and credible methods for building a music tagging system.
Authors
Choi, KeunwooCollections
- Theses [4213]