https://github.com/mdeff/dlaudio
Master thesis: Structured Auto-Encoder with application to Music Genre Recognition (code)
https://github.com/mdeff/dlaudio
auto-encoders deep-learning graphs manifold-learning music-information-retrieval sparse
Last synced: 7 months ago
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Master thesis: Structured Auto-Encoder with application to Music Genre Recognition (code)
- Host: GitHub
- URL: https://github.com/mdeff/dlaudio
- Owner: mdeff
- License: mit
- Created: 2015-05-12T13:47:05.000Z (almost 11 years ago)
- Default Branch: master
- Last Pushed: 2020-04-18T14:25:34.000Z (almost 6 years ago)
- Last Synced: 2024-05-01T15:04:03.180Z (almost 2 years ago)
- Topics: auto-encoders, deep-learning, graphs, manifold-learning, music-information-retrieval, sparse
- Language: Jupyter Notebook
- Homepage: https://infoscience.epfl.ch/record/218019
- Size: 86.9 KB
- Stars: 15
- Watchers: 5
- Forks: 6
- 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 code developed during my master thesis.
Related resources:
* Report:
* Slides:
* Code:
* Experimental results:
* Latex sources of the report: