Predicting Afrobeats Hit Songs Using Spotify Data

• URL: http://arxiv.org/abs/2007.03137v2
• Date: Mon, 10 Aug 2020 13:13:56 GMT
• Title: Predicting Afrobeats Hit Songs Using Spotify Data
• Authors: Adewale Adeagbo
• Abstract summary: 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%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study approached the Hit Song Science problem with the aim of predicting which songs in the Afrobeats genre will become popular among Spotify listeners. A dataset of 2063 songs was generated through the Spotify Web API, with the provided audio features. Random Forest and Gradient Boosting algorithms proved to be successful with approximately F1 scores of 86%.

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)
• SongCreator: Lyrics-based Universal Song Generation [53.248473603201916]
  SongCreator is a song-generation system designed to tackle the challenge of generating songs with both vocals and accompaniment given lyrics. The model features two novel designs: a meticulously designed dual-sequence language model (M) to capture the information of vocals and accompaniment for song generation, and a series of attention mask strategies for DSLM. Experiments demonstrate the effectiveness of SongCreator by achieving state-of-the-art or competitive performances on all eight tasks.
  arXiv  Detail & Related papers  (2024-09-09T19:37:07Z)
• 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)
• Music Recommendation on Spotify using Deep Learning [0.0]
  Hosting about 50 million and 4 billion gigabytes, there is an enormous amount of data generated at Spotify every single day. This paper aims to appropriate filtering using the approach of deep learning for maximum user likeability.
  arXiv  Detail & Related papers  (2023-12-10T07:35:17Z)
• 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)
• GETMusic: Generating Any Music Tracks with a Unified Representation and Diffusion Framework [58.64512825534638]
  Symbolic music generation aims to create musical notes, which can help users compose music. We introduce a framework known as GETMusic, with GET'' standing for GEnerate music Tracks'' GETScore represents musical notes as tokens and organizes tokens in a 2D structure, with tracks stacked vertically and progressing horizontally over time. Our proposed representation, coupled with the non-autoregressive generative model, empowers GETMusic to generate music with any arbitrary source-target track combinations.
  arXiv  Detail & Related papers  (2023-05-18T09:53:23Z)
• 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)
• 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)
• Melody-Conditioned Lyrics Generation with SeqGANs [81.2302502902865]
  We propose an end-to-end melody-conditioned lyrics generation system based on Sequence Generative Adversarial Networks (SeqGAN) We show that the input conditions have no negative impact on the evaluation metrics while enabling the network to produce more meaningful results.
  arXiv  Detail & Related papers  (2020-10-28T02:35:40Z)
• Hit Song Prediction Based on Early Adopter Data and Audio Features [5.88864611435337]
  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.
  arXiv  Detail & Related papers  (2020-10-16T06:42:40Z)

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.