Predicting danceability and song ratings using deep learning and auditory features.

ABSTRACT: Predicting a song's danceability and overall rating poses a significant challenge due to the complex interplay between musical characteristics and listener preferences. In this study, we propose a deep learning framework that jointly addresses the tasks of danceability estimation and popularity prediction. Our model integrates a Bidirectional Long Short-Term Memory (BiLSTM) network to capture sequential and contextual patterns from categorical inputs, alongside a Residual Network (ResNet) that extracts hierarchical representations from numerical auditory features. These complementary feature streams are fused using a cross-attention mechanism, enabling the model to effectively learn intricate relationships across heterogeneous data modalities. Experimental evaluations demonstrate that our

SUBMITTER: Wu W 

PROVIDER: S-EPMC12453869 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature