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Estimating music descriptions using convolutional neural networks| old_uid | 18170 |
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| title | Estimating music descriptions using convolutional neural networks |
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| start_date | 2019/12/19 |
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| schedule | 10h |
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| online | no |
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| summary | In Music Information Retrieval (MIR) and voice processing, the use of
machine learning tools has become in the last few years more and more
standard. Especially, many state-of-the-art systems now rely on the use
of Neural Network. More precisely, we will focus on convolutional
neural networks (ConvNet), a class of neural networks designed for image
analysis.
To apply such networks to sound and music, several problems can arise.
We chose to study 3 of them:
- What is the impact of input representation? Which can of transform
can we use to inform the resolution of MIR problems? To answer those
questions, we will present works don on structure estimation and singing
voice separation.
- How can we gather large amount of labeled data ? We will present
two different strategies: one for singing voice detection using teacher
student paradigm and one for singing voice separation, using signal
processing tools for data augmentation.
- How can we use ConvNet not only to solve problems, but also to
validate solutions? To study this problem, we will present how we design
and evaluate a voice anonymization method in urban sound recordings. |
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| responsibles | Piwowarski |
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