{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["11"],"submitter":["Wu W"],"pubmed_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 "],"journal":["PeerJ. Computer science"],"pagination":["e3009"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12453869"],"repository":["biostudies-literature"],"pubmed_title":["Predicting danceability and song ratings using deep learning and auditory features."],"pmcid":["PMC12453869"],"pubmed_authors":["Wu W"],"additional_accession":[]},"is_claimable":false,"name":"Predicting danceability and song ratings using deep learning and auditory features.","description":"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 ","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025","modification":"2026-04-30T03:16:17.854Z","creation":"2026-04-30T03:11:21.289Z"},"accession":"S-EPMC12453869","cross_references":{"pubmed":["40989462"],"doi":["10.7717/peerj-cs.3009"]}}