Hit Song Prediction Based on Early Adopter Data and Audio Features

• URL: http://arxiv.org/abs/2010.09489v1
• Date: Fri, 16 Oct 2020 06:42:40 GMT
• Title: Hit Song Prediction Based on Early Adopter Data and Audio Features
• Authors: Dorien Herremans, Tom Bergmans
• Abstract summary: This research provides a new strategy for assessing the hit potential of songs. A number of models were developed that use both audio data and social media listening behaviour. The results show that models based on early adopter behaviour perform well when predicting top 20 dance hits.
• Score: 5.88864611435337
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Billions of USD are invested in new artists and songs by the music industry every year. This research provides a new strategy for assessing the hit potential of songs, which can help record companies support their investment decisions. A number of models were developed that use both audio data, and a novel feature based on social media listening behaviour. The results show that models based on early adopter behaviour perform well when predicting top 20 dance hits.

Related papers

• MusicFlow: Cascaded Flow Matching for Text Guided Music Generation [53.63948108922333]
  MusicFlow is a cascaded text-to-music generation model based on flow matching. We leverage masked prediction as the training objective, enabling the model to generalize to other tasks such as music infilling and continuation.
  arXiv  Detail & Related papers  (2024-10-27T15:35:41Z)
• Prevailing Research Areas for Music AI in the Era of Foundation Models [8.067636023395236]
  There has been a surge of generative music AI applications within the past few years. We discuss the current state of music datasets and their limitations. We highlight applications of these generative models towards extensions to multiple modalities and integration with artists' workflow.
  arXiv  Detail & Related papers  (2024-09-14T09:06:43Z)
• MuPT: A Generative Symbolic Music Pretrained Transformer [56.09299510129221]
  We explore the application of Large Language Models (LLMs) to the pre-training of music. To address the challenges associated with misaligned measures from different tracks during generation, we propose a Synchronized Multi-Track ABC Notation (SMT-ABC Notation) Our contributions include a series of models capable of handling up to 8192 tokens, covering 90% of the symbolic music data in our training set.
  arXiv  Detail & Related papers  (2024-04-09T15:35:52Z)
• Beyond Beats: A Recipe to Song Popularity? A machine learning approach [2.6422127672474933]
  This study aims to explore the predictive power of various machine learning models in forecasting song popularity. We employ Ordinary Least Squares (OLS) regression analysis to analyse song characteristics and their impact on popularity. Random Forest emerges as the most effective model, improving prediction accuracy by 7.1% compared to average scores.
  arXiv  Detail & Related papers  (2024-03-01T17:14:41Z)
• MusicRL: Aligning Music Generation to Human Preferences [62.44903326718772]
  MusicRL is the first music generation system finetuned from human feedback. We deploy MusicLM to users and collect a substantial dataset comprising 300,000 pairwise preferences. We train MusicRL-U, the first text-to-music model that incorporates human feedback at scale.
  arXiv  Detail & Related papers  (2024-02-06T18:36:52Z)
• Fairness Through Domain Awareness: Mitigating Popularity Bias For Music Discovery [56.77435520571752]
  We explore the intrinsic relationship between music discovery and popularity bias. We propose a domain-aware, individual fairness-based approach which addresses popularity bias in graph neural network (GNNs) based recommender systems. Our approach uses individual fairness to reflect a ground truth listening experience, i.e., if two songs sound similar, this similarity should be reflected in their representations.
  arXiv  Detail & Related papers  (2023-08-28T14:12:25Z)
• MARBLE: Music Audio Representation Benchmark for Universal Evaluation [79.25065218663458]
  We introduce the Music Audio Representation Benchmark for universaL Evaluation, termed MARBLE. It aims to provide a benchmark for various Music Information Retrieval (MIR) tasks by defining a comprehensive taxonomy with four hierarchy levels, including acoustic, performance, score, and high-level description. We then establish a unified protocol based on 14 tasks on 8 public-available datasets, providing a fair and standard assessment of representations of all open-sourced pre-trained models developed on music recordings as baselines.
  arXiv  Detail & Related papers  (2023-06-18T12:56:46Z)
• An Analysis of Classification Approaches for Hit Song Prediction using Engineered Metadata Features with Lyrics and Audio Features [5.871032585001082]
  This study aims to improve the prediction result of the top 10 hits among Billboard Hot 100 songs using more alternative metadata. Five machine learning approaches are applied, including: k-nearest neighbours, Naive Bayes, Random Forest, Logistic Regression and Multilayer Perceptron. Our results show that Random Forest (RF) and Logistic Regression (LR) with all features outperforms other models, achieving 89.1% and 87.2% accuracy, and 0.91 and 0.93 AUC, respectively.
  arXiv  Detail & Related papers  (2023-01-31T09:48:53Z)
• SpotHitPy: A Study For ML-Based Song Hit Prediction Using Spotify [0.0]
  We gathered a dataset of nearly 18500 hit and non-hit songs. We extracted their audio features using the Spotify Web API. We were able to predict the Billboard success of a song with approximately 86% accuracy.
  arXiv  Detail & Related papers  (2023-01-19T10:13:52Z)
• Exploring the Efficacy of Pre-trained Checkpoints in Text-to-Music Generation Task [86.72661027591394]
  We generate complete and semantically consistent symbolic music scores from text descriptions. We explore the efficacy of using publicly available checkpoints for natural language processing in the task of text-to-music generation. Our experimental results show that the improvement from using pre-trained checkpoints is statistically significant in terms of BLEU score and edit distance similarity.
  arXiv  Detail & Related papers  (2022-11-21T07:19:17Z)
• Predicting Afrobeats Hit Songs Using Spotify Data [0.0]
  A dataset of 2063 songs was generated through the Spotify Web API. Random Forest and Gradient Boosting algorithms proved to be successful with approximately F1 scores of 86%.
  arXiv  Detail & Related papers  (2020-07-07T00:14:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.