Article

A music search engine built upon audio-based and web-based similarity measures

Authors:

Gerhard WidmerAuthors Info & Claims

SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 447 - 454

https://doi.org/10.1145/1277741.1277818

Published: 23 July 2007 Publication History

Abstract

An approach is presented to automatically build a search engine for large-scale music collections that can be queried through natural language. While existing approaches depend on explicit manual annotations and meta-data assigned to the individual audio pieces, we automatically derive descriptions by making use of methods from Web Retrieval and Music Information Retrieval. Based on the ID3 tags of a collection of mp3 files, we retrieve relevant Web pages via Google queries and use the contents of these pages to characterize the music pieces and represent them by term vectors. By incorporating complementary information about acous tic similarity we are able to both reduce the dimensionality of the vector space and improve the performance of retrieval, i.e. the quality of the results. Furthermore, the usage of audio similarity allows us to also characterize audio pieces when there is no associated information found on the Web.

References

[1]

J.-J. Aucouturier. Ten Experiments on the Modelling of Polyphonic Timbre. PhD thesis, University of Paris 6, 2006.

[2]

J.-J. Aucouturier and F. Pachet. Music Similarity Measures: What's the Use? In Proceedings of the 3rd International Conference on Music Information Retrieval, Paris, France, 2002.

[3]

J.-J. Aucouturier, F. Pachet, and M. Sandler. "The Way It Sounds": Timbre Models for Analysis and Retrieval of Music Signals . IEEE Transactions on Multimedia, 7(6):1028--1035, December 2005.

Digital Library

[4]

R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. Addison-Wesley, Reading, Massachusetts, 1999.

Digital Library

[5]

S. Baumann, A. Klüter, and M. Norlien. Using natural language input and audio analysis for a human-oriented mir system. In Proceedings of the 2nd International Conference on Web Delivering of Music, Darmstadt, Germany, 2002.

Digital Library

[6]

P. Cano, M. Koppenberger, N. Wack, et al. An industrial-strength content-based music recommendation system. In Proceedings of 28th Annual International ACM SIGIR Conference, Salvador, Brazil, 2005.

Digital Library

[7]

I. Celino, E. Della Valle, D. Cerizza, and A. Turati. Squiggle: a semantic search engine for indexing and retrieval of multimedia content. In Proceedings of the1st International Workshop on Semantic-enhanced Multimedia Presentation Systems, Athens, Greece, 2006.

[8]

O. Celma, P. Cano, and P. Herrera. Search Sounds: An audio crawler focused on weblogs. In Proceedings of the 7th International Conference on Music Information Retrieval, Victoria, B.C., Canada, 2006.

[9]

O. Celma, M. Ramirez, and P. Herrera. Foafing the music: A music recommendation system based on RSS feeds and user preferences. In Proceedings of the 6th International Conference on Music Information Retrieval, London, UK, 2005.

[10]

P. Ciaccia, M. Patella, and P. Zezula. M-tree: An efficient access method for similarity search in metric spaces. In Proceedings of the 23rd International Conference on Very Large Data Bases, San Francisco, CA, USA, 1997.

Digital Library

[11]

A. Ghias, J. Logan, D. Chamberlin, and B. C. Smith. Query by humming: musical information retrieval in an audio database. In Proceedings of the 3rd ACM International Conference on Multimedia, San Francisco, CA, USA, 1995.

Digital Library

[12]

M. Goto and T. Goto. Musicream: New Music Playback Interface for Streaming, Sticking, and Recalling Musical Pieces. In Proceedings of the 6th International Conference on Music Information Retrieval, London, UK, 2005.

[13]

T. Hofmann. Probabilistic Latent Semantic Indexing. In Proceedings of 22nd Annual International ACM SIGIR Conference, Berkeley, CA, USA, 1999.

Digital Library

[14]

P. Knees, E. Pampalk, and G. Widmer. Artist Classification with Web-based Data. In Proceedings of 5th International Conference on Music Information Retrieval, Barcelona, Spain, 2004.

[15]

P. Knees, M. Schedl, T. Pohle, and G. Widmer. An Innovative Three-Dimensional User Interface for Exploring Music Collections Enriched with Meta-Information from the Web. In Proceedings of the 14th ACM International Conference on Multimedia, Santa Barbara, CA, USA, 2006.

Digital Library

[16]

I. Knopke. Aroooga: An audio search engine for the world wide web. In Proceedings of the International Computer Music Conference, Miami, USA, 2004.

[17]

N. C. Maddage, H. Li, and M. S. Kankanhalli. Music structure based vector space retrieval. In Proceedings of 29th Annual International ACM SIGIR Conference, Seattle, USA, 2006.

Digital Library

[18]

M. Mandel and D. Ellis. Song-Level Features and Support Vector Machines for Music Classification. In Proceedings of the 6th International Conference on Music Information Retrieval, London, UK, 2005.

[19]

F. Pachet and D. Cazaly. A Taxonomy of Musical Genres. In Proceedings of RIAO 2000 Content-Based Multimedia Information Access, Paris, France, 2000.

[20]

E. Pampalk. Computational Models of Music Similarity and their Application to Music Information Retrieval. PhD thesis, Vienna University of Technology, March 2006.

[21]

E. Pampalk and M. Goto. Musicrainbow: A new user interface to discover artists using audio-based similarity and web-based labeling. In Proceedings of the 7th International Conference on Music Information Retrieval, Victoria, B.C., Canada, 2006.

[22]

T. Pohle, P. Knees, M. Schedl, and G. Widmer. Automatically Adapting the Structure of Audio Similarity Spaces. In Proceedings of the 1st Workshop on Learning the Semantics of Audio Signals, Athens, Greece, 2006.

[23]

G. Salton and C. Buckley. Term-weighting approaches in automatic text retrieval. Information Processing and Management, 24(5):513--523, 1988.

Digital Library

[24]

B. Whitman. Learning the meaning of music. PhD thesis, Massachusetts Institute of Technology, 2005.

Digital Library

[25]

B. Whitman and S. Lawrence. Inferring Descriptions and Similarity for Music from Community Metadata. In Proceedings of the 2002 International Computer Music Conference, Gothenburg, Sweden, 2002.

[26]

Y. Yang and J. O. Pedersen. A comparative study on feature selection in text categorization. In Proceedings of the 14th International Conference on Machine Learning, Nashville, USA, 1997.

Digital Library

Cited By

Montanaro MRinaldi ARusso CTommasino C(2024)Using knowledge graphs for audio retrieval: a case study on copyright infringement detectionWorld Wide Web10.1007/s11280-024-01277-027:4Online publication date: 11-Jun-2024
https://doi.org/10.1007/s11280-024-01277-0
Benfenati DMontanaro MRinaldi ARusso CTommasino C(2024)Using Focused Crawlers with Obfuscation Techniques in the Audio Retrieval DomainManagement of Digital EcoSystems10.1007/978-3-031-51643-6_1(3-17)Online publication date: 2-Feb-2024
https://doi.org/10.1007/978-3-031-51643-6_1
Turchet LZanotto CPauwels J(2023)“Give me happy pop songs in C major and with a fast tempo”International Journal of Human-Computer Studies10.1016/j.ijhcs.2023.103007173:COnline publication date: 1-May-2023
https://dl.acm.org/doi/10.1016/j.ijhcs.2023.103007
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Index Terms

A music search engine built upon audio-based and web-based similarity measures
1. Applied computing
  1. Arts and humanities
    1. Sound and music computing
2. Information systems
  1. Information retrieval
    1. Specialized information retrieval
      1. Multimedia and multimodal retrieval
        Music retrieval

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Published In

cover image ACM Conferences

SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval

July 2007

946 pages

ISBN:9781595935977

DOI:10.1145/1277741

General Chairs:
Wessel Kraaij
TNO, The Netherlands
,
Arjen P. de Vries
CWI, The Netherlands
,
Program Chairs:
Charles L. A. Clarke
University of Waterloo, Canada
,
Norbert Fuhr
University of Duisburg-Essen, Germany
,
Noriko Kando
National Institute of Informatics, Japan

Copyright © 2007 ACM.

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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Publication History

Published: 23 July 2007

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SIGIR07

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SIGIR07: The 30th Annual International SIGIR Conference

July 23 - 27, 2007

Amsterdam, The Netherlands

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

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https://doi.org/10.1007/s11280-024-01277-0
Benfenati DMontanaro MRinaldi ARusso CTommasino C(2024)Using Focused Crawlers with Obfuscation Techniques in the Audio Retrieval DomainManagement of Digital EcoSystems10.1007/978-3-031-51643-6_1(3-17)Online publication date: 2-Feb-2024
https://doi.org/10.1007/978-3-031-51643-6_1
Turchet LZanotto CPauwels J(2023)“Give me happy pop songs in C major and with a fast tempo”International Journal of Human-Computer Studies10.1016/j.ijhcs.2023.103007173:COnline publication date: 1-May-2023
https://dl.acm.org/doi/10.1016/j.ijhcs.2023.103007
Montanaro MRinaldi ARusso CTommasino C(2023)A rule-based obfuscating focused crawler in the audio retrieval domainMultimedia Tools and Applications10.1007/s11042-023-16155-683:9(25231-25260)Online publication date: 30-Aug-2023
https://doi.org/10.1007/s11042-023-16155-6
Yakura HNakano TGoto M(2022)An automated system recommending background music to listen to while workingUser Modeling and User-Adapted Interaction10.1007/s11257-022-09325-y32:3(355-388)Online publication date: 18-May-2022
https://doi.org/10.1007/s11257-022-09325-y
Kim AKim M(2020)A Study on Blockchain-based Music Distribution Framework: Focusing on Copyright Protection2020 International Conference on Information and Communication Technology Convergence (ICTC)10.1109/ICTC49870.2020.9289184(1921-1925)Online publication date: 21-Oct-2020
https://doi.org/10.1109/ICTC49870.2020.9289184
Zhang XSmith ADeterding SKhosmood FPirker JApperley T(2019)Retrieving videogame moments with natural language queriesProceedings of the 14th International Conference on the Foundations of Digital Games10.1145/3337722.3341867(1-7)Online publication date: 26-Aug-2019
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Rimaz MElahi MBakhshandegan Moghadam FTrattner CHosseini RTkalčič MPapadopoulos GSamaras GWeibelzahl SJannach DSantos O(2019)Exploring the Power of Visual Features for the Recommendation of MoviesProceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3320435.3320470(303-308)Online publication date: 7-Jun-2019
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Vandana TKalyani NSanthi Sree K(2019)Music Mood Categorization: A SurveyICICCT 2019 – System Reliability, Quality Control, Safety, Maintenance and Management10.1007/978-981-13-8461-5_14(120-132)Online publication date: 28-Jun-2019
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Xambó APauwels JRoma GBarthet MFazekas GCunningham SPicking R(2018)Jam with JamendoProceedings of the Audio Mostly 2018 on Sound in Immersion and Emotion10.1145/3243274.3243291(1-7)Online publication date: 12-Sep-2018
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