Who Will Top the Charts? Multimodal Music Popularity Prediction via Adaptive Fusion of Modality Experts and Temporal Engagement Modeling
Abstract
Predicting a song's commercial success prior to its release remains an open and critical research challenge for the music industry. Early prediction of music popularity informs strategic decisions, creative planning, and marketing. Existing methods suffer from four limitations:(i) temporal dynamics in audio and lyrics are averaged away; (ii) lyrics are represented as a bag of words, disregarding compositional structure and affective semantics; (iii) artist- and song-level historical performance is ignored; and (iv) multimodal fusion approaches rely on simple feature concatenation, resulting in poorly aligned shared representations. To address these limitations, we introduce GAMENet, an end-to-end multimodal deep learning architecture for music popularity prediction. GAMENet integrates modality-specific experts for audio, lyrics, and social metadata through an adaptive gating mechanism. We use audio features from Music4AllOnion processed via OnionEnsembleAENet, a network of autoencoders designed for robust feature extraction; lyric embeddings derived through a large language model pipeline; and newly introduced Career Trajectory Dynamics (CTD) features that capture multi-year artist career momentum and song-level trajectory statistics. Using the Music4All dataset (113k tracks), previously explored in MIR tasks but not popularity prediction, GAMENet achieves a 12% improvement in R^2 over direct multimodal feature concatenation. Spotify audio descriptors alone yield an R^2 of 0.13. Integrating aggregate CTD features increases this to 0.69, with an additional 7% gain from temporal CTD features. We further validate robustness using the SpotGenTrack Popularity Dataset (100k tracks), achieving a 16% improvement over the previous baseline. Extensive ablations confirm the model's effectiveness and the distinct contribution of each modality.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
This paper presents GAMENet, a novel multimodal deep learning framework for predicting music popularity that effectively integrates diverse data sources and temporal dynamics. The technical contributions, particularly the introduction of CTD features and the adaptive gating mechanism, represent a meaningful advancement in the field of music information retrieval and machine learning.
Methodology
The methodology is robust, introducing GAMENet, a multimodal deep learning architecture that effectively combines audio, lyrics, and social metadata through an adaptive gating mechanism. The incorporation of Career Trajectory Dynamics (CTD) features is particularly innovative, addressing the temporal dynamics and historical performance aspects that previous models overlooked. The use of OnionEnsembleAENet for audio feature extraction demonstrates a thoughtful approach to feature engineering, ensuring that the model captures essential characteristics from diverse modalities.
Results & evaluation
The experiments are extensive and well-structured, utilizing two large datasets (Music4All and SpotGenTrack) to validate the model's performance. The reported improvements in R² scores (from 0.13 to 0.69 with CTD features) are significant, indicating that the proposed methods yield meaningful advancements over existing baselines. The ablation studies effectively demonstrate the contribution of each modality, reinforcing the importance of the CTD features in the overall model performance.
Reproducibility
The paper provides detailed descriptions of the datasets, preprocessing steps, and model training procedures, which enhances reproducibility. However, it lacks a direct link to code or a demo, which would further facilitate independent verification of results.
Limitations
One limitation is the reliance on historical data, which may not fully capture future trends in music popularity, especially in a rapidly evolving industry. Additionally, while the model shows strong performance, it may still struggle with songs that have atypical engagement patterns or those that do not fit established trends.
Broader impact
The findings have significant implications for the music industry, offering a tool for predicting song popularity that can inform marketing strategies and creative decisions. The model's ability to integrate various data types could also inspire further research into multimodal learning applications across different domains. This paper presents GAMENet, a novel multimodal deep learning framework for predicting music popularity that effectively integrates diverse data sources and temporal dynamics. The technical contributions, particularly the introduction of CTD features and the adaptive gating mechanism, represent a meaningful advancement in the field of music information retrieval and machine learning.
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