Beyond Beats: A Recipe to Song Popularity? A machine learning approach

• URL: http://arxiv.org/abs/2403.12079v1
• Date: Fri, 1 Mar 2024 17:14:41 GMT
• Title: Beyond Beats: A Recipe to Song Popularity? A machine learning approach
• Authors: Niklas Sebastian, Jung, Florian Mayer,
• Abstract summary: 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.
• Score: 2.6422127672474933
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Music popularity prediction has garnered significant attention in both industry and academia, fuelled by the rise of data-driven algorithms and streaming platforms like Spotify. This study aims to explore the predictive power of various machine learning models in forecasting song popularity using a dataset comprising 30,000 songs spanning different genres from 1957 to 2020. Methods: We employ Ordinary Least Squares (OLS), Multivariate Adaptive Regression Splines (MARS), Random Forest, and XGBoost algorithms to analyse song characteristics and their impact on popularity. Results: Ordinary Least Squares (OLS) regression analysis reveals genre as the primary influencer of popularity, with notable trends over time. MARS modelling highlights the complex relationship between variables, particularly with features like instrumentalness and duration. Random Forest and XGBoost models underscore the importance of genre, especially EDM, in predicting popularity. Despite variations in performance, Random Forest emerges as the most effective model, improving prediction accuracy by 7.1% compared to average scores. Despite the importance of genre, predicting song popularity remains challenging, as observed variations in music-related features suggest complex interactions between genre and other factors. Consequently, while certain characteristics like loudness and song duration may impact popularity scores, accurately predicting song success remains elusive.

Related papers

• PopALM: Popularity-Aligned Language Models for Social Media Trendy Response Prediction [6.979995957338177]
  We study trendy response prediction to automatically generate top-liked user replies to social media events. We propose Popularity-Aligned Language Models (PopALM) to distinguish responses liked by a larger audience through reinforcement learning. In experiments, we build a large-scale Weibo dataset for trendy response prediction, and its results show that PopALM can help boost the performance of advanced language models.
  arXiv  Detail & Related papers  (2024-02-29T08:28:04Z)
• 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)
• ASPEST: Bridging the Gap Between Active Learning and Selective Prediction [56.001808843574395]
  Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain. Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples. In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain.
  arXiv  Detail & Related papers  (2023-04-07T23:51:07Z)
• 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)
• 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)
• Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generation [138.74751744348274]
  We propose Museformer, a Transformer with a novel fine- and coarse-grained attention for music generation. Specifically, with the fine-grained attention, a token of a specific bar directly attends to all the tokens of the bars that are most relevant to music structures. With the coarse-grained attention, a token only attends to the summarization of the other bars rather than each token of them so as to reduce the computational cost.
  arXiv  Detail & Related papers  (2022-10-19T07:31:56Z)
• MATT: A Multiple-instance Attention Mechanism for Long-tail Music Genre Classification [1.8275108630751844]
  Imbalanced music genre classification is a crucial task in the Music Information Retrieval (MIR) field. Most of the existing models are designed for class-balanced music datasets. We propose a novel mechanism named Multi-instance Attention (MATT) to boost the performance for identifying tail classes.
  arXiv  Detail & Related papers  (2022-09-09T03:52:44Z)
• Meta-Learning over Time for Destination Prediction Tasks [53.12827614887103]
  A need to understand and predict vehicles' behavior underlies both public and private goals in the transportation domain. Recent studies have found, at best, only marginal improvements in predictive performance from incorporating temporal information. We propose an approach based on hypernetworks, in which a neural network learns to change its own weights in response to an input.
  arXiv  Detail & Related papers  (2022-06-29T17:58:12Z)
• Context-Based Music Recommendation Algorithm Evaluation [0.0]
  This paper explores 6 machine learning algorithms and their individual accuracy for predicting whether a user will like a song. The algorithms explored include Logistic Regression, Naive Bayes, Sequential Minimal Optimization (SMO), Multilayer Perceptron (Neural Network), Nearest Neighbor, and Random Forest. With the analysis of the specific characteristics of each song provided by the Spotify API, Random Forest is the most successful algorithm for predicting whether a user will like a song with an accuracy of 84%.
  arXiv  Detail & Related papers  (2021-12-16T01:46:36Z)
• Visualizing Ensemble Predictions of Music Mood [4.5383186433033735]
  We show that visualization techniques can effectively convey the popular prediction as well as uncertainty at different music sections along the temporal axis. We introduce a new variant of ThemeRiver, called "dual-flux ThemeRiver", which allows viewers to observe and measure the most popular prediction more easily.
  arXiv  Detail & Related papers  (2021-12-14T18:13:21Z)
• Predicting MOOCs Dropout Using Only Two Easily Obtainable Features from the First Week's Activities [56.1344233010643]
  Several features are considered to contribute towards learner attrition or lack of interest, which may lead to disengagement or total dropout. This study aims to predict dropout early-on, from the first week, by comparing several machine-learning approaches.
  arXiv  Detail & Related papers  (2020-08-12T10:44:49Z)

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.