Master Thesis Online
Machine Learning Applied to Music: Prediction of the Popularity of a Track
Benzerga, Amina 
Promotor(s) :
Geurts, Pierre
Date of defense : 25-Jun-2018/26-Jun-2018 • Permalink : http://hdl.handle.net/2268.2/4606
Details
| Title : | Machine Learning Applied to Music: Prediction of the Popularity of a Track |
| Translated title : | [fr] L’apprentissage automatique appliqué à la musique: prédiction de la popularité de chansons |
| Author : | Benzerga, Amina
|
| Date of defense : | 25-Jun-2018/26-Jun-2018 |
| Advisor(s) : | Geurts, Pierre
|
| Committee's member(s) : | Embrechts, Jean-Jacques
Louppe, Gilles
Lidy, Thomas |
| Language : | English |
| Number of pages : | 101 |
| Keywords : | [en] machine learning [en] time series [en] forecasting |
| Discipline(s) : | Engineering, computing & technology > Computer science |
| Target public : | Other |
| Institution(s) : | Université de Liège, Liège, Belgique |
| Degree: | Master en ingénieur civil en informatique, à finalité spécialisée en "intelligent systems" |
| Faculty: | Master thesis of the Faculté des Sciences appliquées |
Abstract
[en] Machine learning applied to music. We tried, in this thesis, to predict the popularity of tracks. To do so, we used time series forecasting models as well as neural networks and random forest.
File(s)
Document(s)
TFE_résumé.pdf
Description:
Size: 101.3 kB
Format: Adobe PDF
master-thesis-popularity.pdf
Description:
Size: 8.04 MB
Format: Adobe PDF
Annexe(s)
MAE_comp_c_test_hist.png
Description: MAE obtained for the test set for LSTM models
Size: 76.15 kB
Format: image/png
p_vs_a.png
Description: Comparing the number of tracks for which each model performs better than the others.
Size: 15 kB
Format: image/png
pred_1.png
Description: Forecasts obtained using our models are compared to the real values
Size: 107.68 kB
Format: image/png
pred_2.png
Description: Forecasts obtained using our models are compared to the real values
Size: 96.03 kB
Format: image/png
rf_importance.png
Description: Relevant audio features for predicting the trend of a track, only using the audio features
Size: 109.97 kB
Format: image/png
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The University of Liège does not guarantee the scientific quality of these students' works or the accuracy of all the information they contain.