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An industrial-strength content-based music recommendation system

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SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

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https://doi.org/10.1145/1076034.1076185

Published: 15 August 2005 Publication History

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Abstract

We present a metadata free system for the interaction with massive collections of music, the MusicSurfer. MusicSurfer automatically extracts descriptions related to instrumentation, rhythm and harmony from music audio signals. Together with efficient similarity metrics, the descriptions allow navigation of multimillion track music collections in a flexible and efficient way without the need of metadata or human ratings.

References

[1]

http://www.iua.upf.edu/mtg/publications.php

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[2]

http://ismir2004.ismir.net/ISMIR_Contest.html

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[3]

Cano, P., Gómez, E., Gouyon, F., Koppenberger, M., Herrera, P., Ong, B., Serra, X., Streich, S., Wack, N. "ISMIR 2004 Audio Description Contest", in press.

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Cited By

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Mei MBembom OEhmann A(2023)Station and Track Attribute-Aware Music PersonalizationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3610239(1031-1035)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3610239
Allawadi KVij C(2023)A Smart Spotify Assistance and Recommendation System2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)10.1109/InCACCT57535.2023.10141810(286-291)Online publication date: 5-May-2023
https://doi.org/10.1109/InCACCT57535.2023.10141810
Yadav NKumar Singh APal S(2022)Improved self‐attentive Musical Instrument Digital Interface content‐based music recommendation systemComputational Intelligence10.1111/coin.1250138:4(1232-1257)Online publication date: 19-Jan-2022
https://doi.org/10.1111/coin.12501
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Index Terms

An industrial-strength content-based music recommendation system
1. Applied computing
  1. Arts and humanities
    1. Sound and music computing
2. Hardware
  1. Communication hardware, interfaces and storage
    1. Signal processing systems
  2. Robustness
    1. Hardware reliability
      1. Signal integrity and noise analysis

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Published In

SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

August 2005

708 pages

ISBN:1595930345

DOI:10.1145/1076034

General Chairs:
Ricardo Baeza-Yates
University of Chile, Chile
,
Nivio Ziviani
Federal University of Minas Gerais, Brazil
,
Program Chairs:
Gary Marchionini
University of North Carolina, USA
,
Alistair Moffat
University of Melbourne, Australia
,
John Tait
University of Sunderland, UK

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 15 August 2005

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SIGIR05: The 28th ACM/SIGIR International Symposium on Information Retrieval 2005

August 15 - 19, 2005

Salvador, Brazil

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

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Mei MBembom OEhmann A(2023)Station and Track Attribute-Aware Music PersonalizationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3610239(1031-1035)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3610239
Allawadi KVij C(2023)A Smart Spotify Assistance and Recommendation System2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT)10.1109/InCACCT57535.2023.10141810(286-291)Online publication date: 5-May-2023
https://doi.org/10.1109/InCACCT57535.2023.10141810
Yadav NKumar Singh APal S(2022)Improved self‐attentive Musical Instrument Digital Interface content‐based music recommendation systemComputational Intelligence10.1111/coin.1250138:4(1232-1257)Online publication date: 19-Jan-2022
https://doi.org/10.1111/coin.12501
Roy RRai VKumar S(2022)Movie Recommendation System Using Machine Learning Techniques2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)10.1109/ICAC3N56670.2022.10074205(182-187)Online publication date: 16-Dec-2022
https://doi.org/10.1109/ICAC3N56670.2022.10074205
Zhu Y(2020)Combining user interest and feedback in an online book personalized recommendation system2020 7th International Conference on Information Science and Control Engineering (ICISCE)10.1109/ICISCE50968.2020.00089(396-401)Online publication date: Dec-2020
https://doi.org/10.1109/ICISCE50968.2020.00089
Roma GHerrera PNogueira W(2018)Environmental sound recognition using short-time feature aggregationJournal of Intelligent Information Systems10.1007/s10844-017-0481-451:3(457-475)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1007/s10844-017-0481-4
Schedl MKnees PGouyon FCremonesi PRicci FBerkovsky STuzhilin A(2017)New Paths in Music Recommender Systems ResearchProceedings of the Eleventh ACM Conference on Recommender Systems10.1145/3109859.3109934(392-393)Online publication date: 27-Aug-2017
https://dl.acm.org/doi/10.1145/3109859.3109934
Kim NChae WLee Y(2017)Music Recommendation with Temporal Dynamics in Multiple Types of User FeedbackProceedings of the 7th International Conference on Emerging Databases10.1007/978-981-10-6520-0_35(319-328)Online publication date: 14-Oct-2017
https://doi.org/10.1007/978-981-10-6520-0_35
Font FRoma GSerra X(2017)Sound Sharing and RetrievalComputational Analysis of Sound Scenes and Events10.1007/978-3-319-63450-0_10(279-301)Online publication date: 22-Sep-2017
https://doi.org/10.1007/978-3-319-63450-0_10
Ikeda SOku KKawagoe KDesai BAlameida ABernardino J(2016)Music Playlist Recommendation Using Acoustic-Feature TransitionsProceedings of the Ninth International C* Conference on Computer Science & Software Engineering10.1145/2948992.2949005(115-118)Online publication date: 20-Jul-2016
https://dl.acm.org/doi/10.1145/2948992.2949005
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Content-based music audio recommendation

A survey of music similarity and recommendation from music context data

SATIN: a persistent musical database for music information retrieval and a supporting deep learning experiment on song instrumental classification

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