Music Popularity: Metrics, Characteristics, and Audio-Based Prediction

Lee, Junghyuk; Lee, Jong-Seok

doi:10.1109/tmm.2018.2820903

Cited by 48 publications

(20 citation statements)

References 28 publications

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“…Similarly, in the paper [5] authors try to predict the future usage of tags in Stock Exchange websites, where Random Forest gives the best result. In paper [6], authors try to predict the song popularity using acoustic features including MFCC features, but their predictions had room for improvement. An end-to-end deep learning architecture named Hit Music Net is presented in the paper [7], which gives better results than other machine learning algorithms.…”

Section: State Of the Artmentioning

confidence: 99%

Predicting Music Popularity Using Machine Learning Algorithm and Music Metrics Available in Spotify

Sharma¹,

Pareek²,

Pathak³

et al. 2022

JDMS

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The exponential growth of online music streaming has given birth to many new platforms among which, the widely used platform is Spotify. The most popular music streaming app's data can be used to predict the capability of a song to be popular before its release with the help of attributes like loudness, energy, acousticness, etc. which is defined when the song is being made. This study helps to predict the popularity of the song using the song metrics available in Spotify by applying Random Forest classifier, K-Nearest neighbour classifier and Linear Support Vector classifier to compare which of these models can effectively predict the popularity. The results found that Random Forest works the best for predicting popularity with high accuracy, precision, recall and F1-score.

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Section: State Of the Artmentioning

confidence: 99%

Predicting Music Popularity Using Machine Learning Algorithm and Music Metrics Available in Spotify

Sharma¹,

Pareek²,

Pathak³

et al. 2022

JDMS

View full text Add to dashboard Cite

show abstract

“…In [38], a set of metrics and characteristics were proposed to improve the interpretation of how popularity can be measured considering the audio signal including both the complexity of the signal and a collection of Mel Frequency Cepstral Coefficients (MFCCs).…”

Section: Background and Related Researchmentioning

confidence: 99%

A Multimodal End-to-End Deep Learning Architecture for Music Popularity Prediction

et al. 2020

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The continuous evolution of multimedia applications is fostering applied research in order to dynamically enhance the services provided by platforms such as Spotify, Lastfm, or Billboard. Thus, innovative methods for retrieving specific information from large volumes of data related with music arises as a potential challenge within the Music Information Retrieval (MIR) framework. Moreover, despite the existence of several musical-based datasets, there is still a lack of information to properly assess an accurate estimation of the impact or the popularity of a song within a platform. Furthermore, the aforementioned platforms measure the popularity in various manners, thus increasing the difficulties in performing generalized and comparable models. In this paper, the creation of SpotGenTrack Popularity Dataset (SPD) is presented as an alternative solution to existing datasets that will facilitate researchers when comparing and promoting their models. In addition, an innovative multimodal end-to-end Deep Learning architecture named as HitMusicNet is presented for predicting popularity in music recordings. Experiments conducted show that the proposed architecture outperforms previous studies in the State-of-the-Art by incorporating three main modalities to the analysis, such as audio, lyrics and meta-data as well as a preliminary compression stage via autoencoder to better the capability of the model when predicting the popularity.

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“…[33] used multimodal cues (audio and video) for the automatic prediction of personality traits of vloggers. In [34], the popularity of music was predicted in terms of eight popularity metrics using mel‐frequency cepstrum coefficient into a trained support vector machine classifier. Tomasz et al.…”

Section: Introductionmentioning

confidence: 99%

Prediction of instantaneous likeability of advertisements using deep learning

Saha

Rahman

Islam

et al. 2021

Cognitive Comp and Systems

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The degree to which advertisements are successful is of prime concern for vendors in highly competitive global markets. Given the astounding growth of multimedia content on the internet, online marketing has become another form of advertising. Researchers consider advertisement likeability a major predictor of effective market penetration. An algorithm is presented to predict how much an advertisement clip will be liked with the aid of an end-to-end audiovisual feature extraction process using cognitive computing technology. Specifically, the usefulness of different spatial and time-domain deeplearning architectures such as convolutional neural and long short-term memory networks is investigated to predict the frame-by-frame instantaneous and root mean square likeability of advertisement clips. A data set named the 'BUET Advertisement Likeness Data Set', containing annotations of frame-wise likeability scores for various categories of advertisements, is also introduced. Experiments with the developed database show that the proposed algorithm performs better than existing methods in terms of commonly used performance indices at the expense of slightly increased computational complexity.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Music Popularity: Metrics, Characteristics, and Audio-Based Prediction

Cited by 48 publications

References 28 publications

Predicting Music Popularity Using Machine Learning Algorithm and Music Metrics Available in Spotify

Predicting Music Popularity Using Machine Learning Algorithm and Music Metrics Available in Spotify

A Multimodal End-to-End Deep Learning Architecture for Music Popularity Prediction

Prediction of instantaneous likeability of advertisements using deep learning

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