biostudies-literatureUnknown11Wu WPredicting 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 PeerJ. Computer sciencee3009https://www.ebi.ac.uk/biostudies/studies/S-EPMC12453869biostudies-literaturePredicting danceability and song ratings using deep learning and auditory features.PMC12453869Wu WfalsePredicting danceability and song ratings using deep learning and auditory features.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 2025-01-01T00:00:00Z20252026-04-30T03:16:17.854Z2026-04-30T03:11:21.289ZS-EPMC124538694098946210.7717/peerj-cs.3009