{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/hit-song-prediction-for-pop-music-by-siamese","title":"Hit Song Prediction for Pop Music by Siamese CNN with Ranking Loss","arxiv_id":"1710.10814","date":"2017-10-30","proceeding":null,"authors":["Lang-Chi Yu","Yi-Hsuan Yang","Yun-Ning Hung","Yi-An Chen"],"abstract":"A model for hit song prediction can be used in the pop music industry to\nidentify emerging trends and potential artists or songs before they are\nmarketed to the public. While most previous work formulates hit song prediction\nas a regression or classification problem, we present in this paper a\nconvolutional neural network (CNN) model that treats it as a ranking problem.\nSpecifically, we use a commercial dataset with daily play-counts to train a\nmulti-objective Siamese CNN model with Euclidean loss and pairwise ranking loss\nto learn from audio the relative ranking relations among songs. Besides, we\ndevise a number of pair sampling methods according to some empirical\nobservation of the data. Our experiment shows that the proposed model with a\nsampling method called A/B sampling leads to much higher accuracy in hit song\nprediction than the baseline regression model. Moreover, we can further improve\nthe accuracy by using a neural attention mechanism to extract the highlights of\nsongs and by using a separate CNN model to offer high-level features of songs.","url_abs":"http://arxiv.org/abs/1710.10814v1","url_pdf":"http://arxiv.org/pdf/1710.10814v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"hit-song-prediction-for-pop-music-by-siamese","repo_url":"https://github.com/Irisfee/spotify_analysis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"hit-song-prediction-for-pop-music-by-siamese","repo_url":"https://github.com/OckhamsRazor/HSP_CNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}