Predicting Hit Music using MIDI features and Machine Learning

被引：0

作者：

Rajyashree, R. ^{[1
]}

Anand, Anmol ^{[1
]}

Soni, Yash ^{[1
]}

Mahajan, Harshitaa ^{[1
]}

机构：

[1] SRM Inst Sci & Technol, Dept Comp Sci & Engn, Chennai, Tamil Nadu, India

来源：

PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON COMMUNICATION AND ELECTRONICS SYSTEMS (ICCES 2018) | 2018年

关键词：

MIDI; !text type='jS']jS[!/text]ymbolic; Million Song Database; Musical Information Retrieval; Naive Bayes; Random Forest; Feedforward Propagation; Back Propagation;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper intends to analyze metadata and audio analysis features from a random sample of one million popular tracks, available in the Million Song Dataset (MSD), and assess their potential of making it into the Billboard hot 100 song list. Through the use of various machine learning different models, we can determine the interaction and importance of different variables over time and their effects on the success on the Billboard charts. With such knowledge, we can access and identify the past music trends and help producers with the steps to create the perfect commercially successful song.

引用

页码：94 / 98

页数：5

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