Metadata and audio features help predict online audio popularity
Predicting Audio Popularity: A Comparative Study of Metadata and Acoustic Features Across Music and Sound Effects
SoundMachine Learning
Summary
Some sounds like music or effects get downloaded and rated a lot, but predicting which ones will be popular is tricky. The authors studied if details about the audio files (metadata) or the actual sound properties (acoustic features) can predict popularity. They found that metadata usually works best for guessing popularity, but the sound itself also holds useful clues. They used computer programs like Random Forests and Neural Networks to make these predictions.
What this means in practice
- •For online audio platform managers: Improve content ranking by predicting popularity using metadata and acoustic audio features.
- •For sound designers: Understand which audio characteristics influence popularity to guide creating appealing sound effects.
Authors
Ikram El Hadi, Nihad Elbari, Kawtar Tlemçani, Théo Mariotte, Meysam Shamsi
Abstract
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 provide the most informative representation for predicting popularity. Nevertheless, acoustic features alone achieve performance significantly above chance level, demonstrating that intrinsic properties of the audio signal contribute to popularity prediction.