Music Data Analysis: A State-of-the-art Survey

TL;DR

This survey reviews current state-of-the-art techniques in music data analysis, covering machine learning, social network analysis, and semantic web approaches, highlighting data sources, methodologies, and research use cases.

Contribution

It provides a comprehensive taxonomy of methods, data sources, and applications in music data analysis, summarizing recent advances and research directions.

Findings

Diverse machine learning techniques are applied to music data.

Social network analysis reveals user interaction patterns.

Semantic web approaches enhance music information retrieval.

Abstract

Music accounts for a significant chunk of interest among various online activities. This is reflected by wide array of alternatives offered in music related web/mobile apps, information portals, featuring millions of artists, songs and events attracting user activity at similar scale. Availability of large scale structured and unstructured data has attracted similar level of attention by data science community. This paper attempts to offer current state-of-the-art in music related analysis. Various approaches involving machine learning, information theory, social network analysis, semantic web and linked open data are represented in the form of taxonomy along with data sources and use cases addressed by the research community.