A Model for Predicting Music Popularity on Streaming Platforms

Araujo, Carlos V.S.; Cristo, Marco; Giusti, Rafael

doi:10.22456/2175-2745.107021

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…Reference [28] evaluated the potential of songs on streaming platforms to become viral and sustain popularity. Reference [29] used classifiers to predict the presence of songs in Spotify's Top-50, focusing on audio features. Reference [30] introduced a new dataset and deep learning architecture for predicting music popularity.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Incremental Network for Estimating Chinese Musical Vogue Patterns of a Given Artist Over Time

Hou,

Zhang

2024

IEEE Access

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Estimating musical future trends is critical for the Chinese music industry, as it assists artists, producers, record labels, and investors in discerning public musical preferences. This knowledge facilitates market strategy optimization, recommendation system improvement, inventory management, and provides informed support for advertising and sponsorships. The benefits extend beyond enhancing user experience and contributing to cultural studies, as they are also invaluable for the discovery of emerging artists and the sustainable growth of the entire Chinese music ecosystem. Nonetheless, existing models for prolonged musical future trend estimation have been challenged by the severe decay of past information. Inspired by the concept of incremental learning, we propose the Dynamic Incremental Network (DINet). DINet capitalizes on high-dimensional sequential information embedded in the memory states of trained sequence models. It dynamically integrates information from both previous and current time steps when constructing the model to estimate the next time step's input sequence, enabling the incremental transmission of past information and addressing the decay problem in prolonged musical future trend estimation. Leveraging an open-source dataset from a musical pattern estimation contest, ablation experiments and comparative analyses have shown that DINet not only surpasses traditional and baseline models but also outperforms existing musical future trend estimation models, demonstrating its superior performance. In the future, we intend to employ the proposed Dynamic Incremental Method (DIM)an Occam's razor (simple yet effective) mechanism-to further enhance the performance of state-of-the-art musical and other sequential estimation tasks.

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Section: Related Workmentioning

confidence: 99%

Dynamic Incremental Network for Estimating Chinese Musical Vogue Patterns of a Given Artist Over Time

Hou,

Zhang

2024

IEEE Access

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“…Evaluasi eksperimental didasarkan pada validasi (80 persen untuk pelatihan dan 20 persen untuk pengujian) dengan akurasi maksimum 60,17 persen, diperoleh dari Gaussian Naïve Bayes. Penelitian lain yang mendukung penelitian ini adalah penelitian yang diusulkan di studi [8].…”

Section: Kajian Pustakaunclassified

Komparasi linear regression, random forest regression, dan multilayer perceptron regression untuk prediksi tren musik TikTok

Soraya,

Hendry

2023

AITI

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Prediksi korelasi feature audio terhadap lagu yang populer di TikTok merupakan hal penting dalam industri musik. Dengan bekal data yang memiliki beberapa feature audio maka dilakukan penelitian menggunakan metode Linear Regression (LR), Random Forest Regression (RFR), dan Multilayer Perceptron Regression (MLP Regression) untuk membandingkan model yang dapat memprediksi popularitas secara efektif dan feature yang mempengaruhi popularitas lagu di TikTok. Selain itu dilakukan juga Exploratory Data Analysis (EDA) untuk mendapatkan insight data. Hasil dari proses EDA yaitu popularitas lagu terbanyak berada pada range 40-80, durasi lagu antara 2-3 menit, feature loudness berkorelasi positif dengan energy, demikian juga antara artist_pop dan track_pop. Set feature importance pada model LR dan RFR untuk feature target track_pop adalah artist_pop, loudness, dan duration_ms. Metode LR memiliki hasil terbaik untuk dataset yang dipakai, dengan MSE sebesar 0.0313, RMSE sebesar 0.177, dan MAE sebesar 0.118.

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Hit songs prediction: A review on machine learning perspective

Yap,

Raheem

2024

WOMEN IN PHYSICS: 7th IUPAP International Conference on Women in Physics

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A Model for Predicting Music Popularity on Streaming Platforms

Cited by 6 publications

References 11 publications

Dynamic Incremental Network for Estimating Chinese Musical Vogue Patterns of a Given Artist Over Time

Dynamic Incremental Network for Estimating Chinese Musical Vogue Patterns of a Given Artist Over Time

Komparasi linear regression, random forest regression, dan multilayer perceptron regression untuk prediksi tren musik TikTok

Hit songs prediction: A review on machine learning perspective

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