Emotional classification of music using neural networks with the MediaEval dataset
The proven ability of music to transmit emotions provokes the increasing interest in the development of new algorithms for music emotion recognition (MER). In this work, we present an automatic system of emotional classification of music by implementing a neural network. This work is based on a prev...
| Autores principales: | , , |
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| Formato: | Articulo Preprint |
| Lenguaje: | Inglés |
| Publicado: |
2020
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/127098 |
| Aporte de: |
| id |
I19-R120-10915-127098 |
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| record_format |
dspace |
| institution |
Universidad Nacional de La Plata |
| institution_str |
I-19 |
| repository_str |
R-120 |
| collection |
SEDICI (UNLP) |
| language |
Inglés |
| topic |
Ciencias Informáticas Music emotion recognition (MER) Emotion classification Prediction Music features Multilayer Perceptron |
| spellingShingle |
Ciencias Informáticas Music emotion recognition (MER) Emotion classification Prediction Music features Multilayer Perceptron Ospitia Medina, Yesid Beltrán, José Ramón Baldassarri, Sandra Emotional classification of music using neural networks with the MediaEval dataset |
| topic_facet |
Ciencias Informáticas Music emotion recognition (MER) Emotion classification Prediction Music features Multilayer Perceptron |
| description |
The proven ability of music to transmit emotions provokes the increasing interest in the development of new algorithms for music emotion recognition (MER). In this work, we present an automatic system of emotional classification of music by implementing a neural network. This work is based on a previous implementation of a dimensional emotional prediction system in which a multilayer perceptron (MLP) was trained with the freely available MediaEval database. Although these previous results are good in terms of the metrics of the prediction values, they are not good enough to obtain a classification by quadrant based on the valence and arousal values predicted by the neural network, mainly due to the imbalance between classes in the dataset. To achieve better classification values, a pre-processing phase was implemented to stratify and balance the dataset. Three different classifiers have been compared: linear support vector machine (SVM), random forest, and MLP. The best results are obtained with the MLP. An averaged F-measure of 50% is obtained in a four-quadrant classification schema. Two binary classification approaches are also presented: one vs. rest (OvR) approach in four-quadrants and binary classifier in valence and arousal. The OvR approach has an average F-measure of 69%, and the second one obtained F-measure of 73% and 69% in valence and arousal respectively. Finally, a dynamic classification analysis with different time windows was performed using the temporal annotation data of the MediaEval database. The results obtained show that the classification F-measures in four quadrants are practically constant, regardless of the duration of the time window. Also, this work reflects some limitations related to the characteristics of the dataset, including size, class balance, quality of the annotations, and the sound features available. |
| format |
Articulo Preprint |
| author |
Ospitia Medina, Yesid Beltrán, José Ramón Baldassarri, Sandra |
| author_facet |
Ospitia Medina, Yesid Beltrán, José Ramón Baldassarri, Sandra |
| author_sort |
Ospitia Medina, Yesid |
| title |
Emotional classification of music using neural networks with the MediaEval dataset |
| title_short |
Emotional classification of music using neural networks with the MediaEval dataset |
| title_full |
Emotional classification of music using neural networks with the MediaEval dataset |
| title_fullStr |
Emotional classification of music using neural networks with the MediaEval dataset |
| title_full_unstemmed |
Emotional classification of music using neural networks with the MediaEval dataset |
| title_sort |
emotional classification of music using neural networks with the mediaeval dataset |
| publishDate |
2020 |
| url |
http://sedici.unlp.edu.ar/handle/10915/127098 |
| work_keys_str_mv |
AT ospitiamedinayesid emotionalclassificationofmusicusingneuralnetworkswiththemediaevaldataset AT beltranjoseramon emotionalclassificationofmusicusingneuralnetworkswiththemediaevaldataset AT baldassarrisandra emotionalclassificationofmusicusingneuralnetworkswiththemediaevaldataset |
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Repositorios |
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