aiqbench analysis
This paper applies standard supervised models (Random Forest, XGBoost, a neural network) to predict downloads and user ratings for music and sound effects, comparing metadata against eGeMAPS acoustic features. Metadata consistently outperforms audio features, but acoustic features still beat chance, so the signal is real though modest. For ML readers, the practical takeaway is that platform metadata is the strongest predictor, and audio-based models are best treated as a complement rather than a replacement.
Predicting Audio Popularity: A Comparative Study of Metadata and Acoustic Features Across Music and Sound Effects
arXiv:2610.16809v1 Announce Type: cross 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.
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.
Sound (cs.SD); Machine Learning (cs.LG)
arXiv:2610.16809 [cs.SD]
(or arXiv:2610.16809v1 [cs.SD] for this version)
https://doi.org/10.48550/arXiv.2610.16809 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 220 words
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