https://github.com/mdeff/dlaudio_results
Master thesis: Structured Auto-Encoder with application to Music Genre Recognition (results)
https://github.com/mdeff/dlaudio_results
auto-encoders deep-learning music-information-retrieval
Last synced: 8 months ago
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Master thesis: Structured Auto-Encoder with application to Music Genre Recognition (results)
- Host: GitHub
- URL: https://github.com/mdeff/dlaudio_results
- Owner: mdeff
- License: mit
- Created: 2015-06-11T19:52:55.000Z (almost 11 years ago)
- Default Branch: master
- Last Pushed: 2020-04-18T15:07:08.000Z (about 6 years ago)
- Last Synced: 2025-03-16T01:13:21.811Z (about 1 year ago)
- Topics: auto-encoders, deep-learning, music-information-retrieval
- Language: Jupyter Notebook
- Homepage: https://infoscience.epfl.ch/record/218019
- Size: 17.7 MB
- Stars: 2
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# Master thesis: Structured Auto-Encoder with application to Music Genre Recognition
[Michaël Defferrard](https://deff.ch).
Supervized by [Xavier Bresson](https://www.ntu.edu.sg/home/xbresson),
[Johan Paratte](https://www.linkedin.com/in/johan-paratte-a2070039),
[Pierre Vandergheynst](https://people.epfl.ch/pierre.vandergheynst).
> In this work, we present a technique that learns discriminative audio
> features for Music Information Retrieval (MIR). The novelty of the proposed
> technique is to design auto-encoders that make use of data structures to
> learn enhanced sparse data representations. The data structure is borrowed
> from the Manifold Learning field, that is data are supposed to be sampled
> from smooth manifolds, which are here represented by graphs of proximities of
> the input data. As a consequence, the proposed auto-encoders finds sparse
> data representations that are quite robust w.r.t. perturbations. The model is
> formulated as a non-convex optimization problem. However, it can be
> decomposed into iterative sub-optimization problems that are convex and for
> which well-posed iterative schemes are provided in the context of the Fast
> Iterative Shrinkage-Thresholding (FISTA) framework. Our numerical experiments
> show two main results. Firstly, our graph-based auto-encoders improve the
> classification accuracy by 2% over the auto-encoders without graph structure
> for the popular GTZAN music dataset. Secondly, our model is significantly
> more robust as it is 8% more accurate than the standard model in the presence
> of 10% of perturbations.
## Content
This repository contains the results obtained during my master thesis.
Related resources:
* Report:
* Slides:
* Code:
* Experimental results:
* Latex sources of the report: