S. Gupta. (2014)cite arxiv:1411.5014Comment: 10 pages, 6 figures.
Zusammenfassung
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.
%0 Conference Paper
%1 gupta2014music
%A Gupta, Shubhanshu
%D 2014
%K Analysis Big Data Information Learning Linked Machine Music Network Open Retrieval Semantic Social Theory Web
%T Music Data Analysis: A State-of-the-art Survey
%U http://arxiv.org/abs/1411.5014
%X 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.
@inproceedings{gupta2014music,
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.},
added-at = {2015-01-06T21:37:37.000+0100},
author = {Gupta, Shubhanshu},
biburl = {https://www.bibsonomy.org/bibtex/290fdbdb5de9404a712fc304b69b8fb85/shubhanshugupta},
description = {Music Data Analysis: A State-of-the-art Survey},
interhash = {90f1eb3445d519647e80dc0be7091d67},
intrahash = {90fdbdb5de9404a712fc304b69b8fb85},
keywords = {Analysis Big Data Information Learning Linked Machine Music Network Open Retrieval Semantic Social Theory Web},
note = {cite arxiv:1411.5014Comment: 10 pages, 6 figures},
timestamp = {2015-01-06T21:37:37.000+0100},
title = {Music Data Analysis: A State-of-the-art Survey},
url = {http://arxiv.org/abs/1411.5014},
year = 2014
}