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Music similarity and retrieval

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SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval

Page 1125

https://doi.org/10.1145/2484028.2484193

Published: 28 July 2013 Publication History

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Abstract

This tutorial serves as an introductory course to the field of and state-of-the-art in music information retrieval (MIR) and in particular to music similarity estimation which is an essential component of music retrieval. Apart from explaining approaches that estimate similarity based on acoustic properties of an audio signal, we review methods that exploit (mostly textual) meta-data from the Web to build representations of music then used for similarity calculation. Additionally, topics such as (large-scale) music indexing, information extraction for music, personalization in music retrieval, and evaluation of MIR systems are addressed.

References

[1]

M. Casey, R. Veltkamp, M. Goto, M. Leman, C. Rhodes, and M. Slaney. Content-Based Music Information Retrieval: Current Directions and Future Challenges. Proc IEEE, 96:668--696, April 2008.

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[2]

P. Knees and M. Schedl. A Survey of Music Similarity and Recommendation from Music Context Data. ACM TOMCCAP, 2013. Accepted for publication.

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[3]

M. Schedl and P. Knees. Personalization in Multimodal Music Retrieval. In Proc Workshop AMR, 2011.

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Cited By

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Schoder JBücker HBötticher AKarmann R(2022)MusicDress: A Heterogeneous Dataset for Comparing Music Recommender Systems2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS)10.1109/SNAMS58071.2022.10062594(1-4)Online publication date: 29-Nov-2022
https://doi.org/10.1109/SNAMS58071.2022.10062594
Yan QDing CYin JLv Y(2015)Improving music auto-tagging with trigger-based context model2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP.2015.7178006(434-438)Online publication date: Apr-2015
https://doi.org/10.1109/ICASSP.2015.7178006

Index Terms

Music similarity and retrieval
1. Applied computing
  1. Arts and humanities
    1. Sound and music computing

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Published In

SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval

July 2013

1188 pages

ISBN:9781450320344

DOI:10.1145/2484028

General Chairs:
Gareth J.F. Jones
Dublin City University, Ireland
,
Páraic Sheridan
Dublin City University, Ireland
,
Program Chairs:
Diane Kelly
University of North Carolina, Chapel Hill, USA
,
Maarten de Rijke
University of Amsterdam, The Netherlands
,
Tetsuya Sakai
Microsoft Research Asia, China

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 July 2013

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Tutorial

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SIGIR '13

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SIGIR

SIGIR '13: The 36th International ACM SIGIR conference on research and development in Information Retrieval

July 28 - August 1, 2013

Dublin, Ireland

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SIGIR '13 Paper Acceptance Rate 73 of 366 submissions, 20%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

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Schoder JBücker HBötticher AKarmann R(2022)MusicDress: A Heterogeneous Dataset for Comparing Music Recommender Systems2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS)10.1109/SNAMS58071.2022.10062594(1-4)Online publication date: 29-Nov-2022
https://doi.org/10.1109/SNAMS58071.2022.10062594
Yan QDing CYin JLv Y(2015)Improving music auto-tagging with trigger-based context model2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP.2015.7178006(434-438)Online publication date: Apr-2015
https://doi.org/10.1109/ICASSP.2015.7178006

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A survey of music similarity and recommendation from music context data

Towards a Class-Based Representation of Perceptual Tempo for Music Retrieval

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