Date of Award
12-2026
Document Type
Project
Degree Name
Master of Science in Computer Science
Department
School of Computer Science and Engineering
First Reader/Committee Chair
Dr. Yan Zhang
Abstract
This study compares engineered acoustic features with spectrogram based convolutional neural networks for static Valence and Arousal prediction on the 1,802-song DEAM dataset. Random Forest and XGBoost use 520 dimensional openSMILE features, while the CNN learns from 30-second log-mel spectrograms. The evaluation uses five saved song-level train, validation, and test partitions. The original XGBoost baseline achieves mean RMSE of 0.780 for Valence and 0.793 for Arousal, below every tested CNN configuration on both targets in all five runs. Increasing the CNN to 101,506 parameters lowers its mean RMSE to 0.837 and 0.855, respectively; a further increase to 403,714 parameters gives little additional benefit. A ten-configuration sweep for each tree model places the original settings among several competitive choices. Auxiliary analyses examine feature importance, prediction averaging, and linear recalibration. These results support openSMILE features with XGBoost as a practical baseline for this song-level prediction task.
Recommended Citation
Lyu, Xiaoyu, "ENGINEERED ACOUSTIC FEATURES AND NEURAL NETWORKS FOR MUSIC EMOTION RECOGNITION: AN EMPIRICAL COMPARISON ON THE DEAM DATASET" (2026). Electronic Theses, Projects, and Dissertations. 2537.
https://scholarworks.lib.csusb.edu/etd/2537