DSpace Collection:http://hdl.handle.net/11422/3372026-07-25T23:46:09Z2026-07-25T23:46:09ZImpactos de eventos extremos de calor sobre a prematuridade: uma abordagem estatística aplicada à saúde públicahttp://hdl.handle.net/11422/297272026-07-22T03:00:13Z2026-05-26T00:00:00ZTitle: Impactos de eventos extremos de calor sobre a prematuridade: uma abordagem estatística aplicada à saúde pública
Author(s)/Inventor(s): André, João Francisco Fortes
Advisor: Landim, Flávia Maria Pinto Ferreira
Publisher: Universidade Federal do Rio de Janeiro
Type: Trabalho de conclusão de graduação2026-05-26T00:00:00ZMachine learning methods in music emotion recognitionhttp://hdl.handle.net/11422/296852026-07-19T03:00:09Z2025-01-01T00:00:00ZTitle: Machine learning methods in music emotion recognition
Author(s)/Inventor(s): Dessabato, Karolayne Pereira
Advisor: Carvalho, Hugo Tremonte de
Abstract: Music Emotion Recognition (MER), an area within Musical Information Retrieval
(MIR), studies the emotions evoked in listeners by music. We address MER as a re
gression task, with the objective of predicting the emotional content of music (encoded
in arousal and valence) from acoustic features extracted from the waveform. We apply
an interpretable machine learning technique, investigating the role of these features in
predicting the target variables. Initially, a random forest model is trained on the DEAM
dataset (MediaEval Database for Emotional Analysis of Music). Then, we use the con
cept of Shapley values to interpret the role of each variable in the predictions made by
this model. Finally, we extract the most significant features from the DEAM dataset to
predict arousal and valence, thus enhancing the interpretability of the model employed.
Additionally, we explore a dynamic linear model approach to gain further insights
into the relationships between features and response variables. This method allows for a
potentially “less black-box” and more interpretable representation of the problem. Prin
cipal Component Analysis (PCA) is also utilized to analyze the structure of features in
the dataset, providing a more comprehensive understanding of the key variables influ
encing MER predictions. By integrating these approaches, we aim to enhance both the
predictive performance and interpretability of the models, offering meaningful insights
into the most relevant features that drive emotional responses in music.
Publisher: Universidade Federal do Rio de Janeiro
Type: Dissertação2025-01-01T00:00:00ZInferência variacional aplicada a modelos de misturahttp://hdl.handle.net/11422/276472026-05-16T03:08:52Z2025-08-01T00:00:00ZTitle: Inferência variacional aplicada a modelos de mistura
Author(s)/Inventor(s): Soares, Iran Cruz
Advisor: Carvalho, Hugo Tremonte de
Publisher: Universidade Federal do Rio de Janeiro
Type: Trabalho de conclusão de graduação2025-08-01T00:00:00Z