Predicting Audio Popularity: A Comparative Study of Metadata and Acoustic Features Across Music and Sound Effects
This study addresses the prediction of audio popularity on an online sharing platform by leveraging both metadata and acoustic feature representations. Two distinct categories of audio content are considered: music and sound effects. Popularity is formulated as a supervised classification task based on two target variables: the number of downloads and the average user rating. We conduct a comparative evaluation of multiple machine learning models, including Random Forest, XGBoost, and a Neural Network model. Experiments are performed across different feature sets, including metadata, platform-provided descriptors, and eGeMAPS acoustic features. Results indicate that metadata consistently ...
| Authors | Ikram El Hadi, Nihad Elbari, Kawtar Tlemçani et al. |
|---|---|
| Primary category | cs.SD |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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