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A practical query-by-humming system for a large music database

Authors:

Yuichi Nishihara,

Masashi Yamamuro,

Kazuhiko KushimaAuthors Info & Claims

MULTIMEDIA '00: Proceedings of the eighth ACM international conference on Multimedia

Pages 333 - 342

https://doi.org/10.1145/354384.354520

Published: 30 October 2000 Publication History

Abstract

A music retrieval system that accepts hummed tunes as queries is described in this paper. This system uses similarity retrieval because a hummed tune may contain errors. The retrieval result is a list of song names ranked according to the closeness of the match. Our ultimate goal is that the correct song should be first on the list. This means that eventually our system's similarity retrieval should allow for only one correct answer.

The most significant improvement our system has over general query-by-humming systems is that all processing of musical information is done based on beats instead of notes. This type of query processing is robust against queries generated from erroneous input. In addition, acoustic information is transcribed and converted into relative intervals and is used for making feature vectors. This increases the resolution of the retrieval system compared with other general systems, which use only pitch direction information.

The database currently holds over 10,000 songs, and the retrieval time is at most one second. This level of performance is mainly achieved through the use of indices for retrieval. In this paper, we also report on the results of music analyses of the songs in the database. Based on these results, new technologies for improving retrieval accuracy, such as partial feature vectors and or'ed retrieval among multiple search keys, are proposed. The effectiveness of these technologies is evaluated quantitatively, and it is found that the retrieval accuracy increases by more than 20% compared with the previous system [9]. Practical user interfaces for the system are also described.

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Cited By

Kurihara KShoji YFujita SDürst M(2021)Learning to Rank-based Approach for Movie Search by Keyword Query and Example QueryThe 23rd International Conference on Information Integration and Web Intelligence10.1145/3487664.3487779(137-145)Online publication date: 29-Nov-2021
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Published In

cover image ACM Conferences

MULTIMEDIA '00: Proceedings of the eighth ACM international conference on Multimedia

October 2000

523 pages

ISBN:1581131984

DOI:10.1145/354384

Chairmen:
Shahram Ghandeharizadeh
USC
,
Shih-Fu Chang
Columbia Univ., New York, NY
,
Stephen Fischer
GMD-IPSI
,
Joseph A. Konstan
Univ. of Minnesota
,
Klara Nahrstedt
Univ. of Illinois, Urbana-Champaign

Copyright © 2000 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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Published: 30 October 2000

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Kurihara KShoji YFujita SDürst M(2021)Learning to Rank-based Approach for Movie Search by Keyword Query and Example QueryThe 23rd International Conference on Information Integration and Web Intelligence10.1145/3487664.3487779(137-145)Online publication date: 29-Nov-2021
https://dl.acm.org/doi/10.1145/3487664.3487779
Engeln LLe NMcGinity MGroh R(2021)Similarity Analysis of Visual Sketch-based Search for SoundsProceedings of the 16th International Audio Mostly Conference10.1145/3478384.3478423(101-108)Online publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1145/3478384.3478423
Chen PDing JHuang JTseng T(2020)Accurate Onset Detection Algorithm using Feature-Layer-Based Deep Learning Architecture2020 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS45731.2020.9181255(1-5)Online publication date: Oct-2020
https://doi.org/10.1109/ISCAS45731.2020.9181255
Kempfert KWong S(2020)Where does Haydn end and Mozart begin? Composer classification of string quartetsJournal of New Music Research10.1080/09298215.2020.1814822(1-20)Online publication date: 2-Sep-2020
https://doi.org/10.1080/09298215.2020.1814822
Ferraro ALemström KPage K(2018)On large-scale genre classification in symbolically encoded music by automatic identification of repeating patternsProceedings of the 5th International Conference on Digital Libraries for Musicology10.1145/3273024.3273035(34-37)Online publication date: 28-Sep-2018
https://dl.acm.org/doi/10.1145/3273024.3273035
Chen PZhao LXin ZQiang YZhang MLi T(2016)A Scheme of MIDI Music Emotion Classification Based on Fuzzy Theme Extraction and Neural Network2016 12th International Conference on Computational Intelligence and Security (CIS)10.1109/CIS.2016.0079(323-326)Online publication date: Dec-2016
https://doi.org/10.1109/CIS.2016.0079
Li LShi J(2015)Design and Realization of Music Retrieval System Based on Feature ContentMATEC Web of Conferences10.1051/matecconf/2015220102422(01024)Online publication date: 9-Jul-2015
https://doi.org/10.1051/matecconf/20152201024
Schedl MGómez EUrbano J(2014)Music Information RetrievalFoundations and Trends in Information Retrieval10.1561/15000000428:2-3(127-261)Online publication date: 12-Sep-2014
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Kosugi NShimizu SSasaki SDiep NKiyoki Y(2014)Music Retrieval and Adjustment Technique to Support and Motivate Ergotherapy and Daily ExercisesProceedings of the 16th International Conference on Information Integration and Web-based Applications & Services10.1145/2684200.2684313(385-394)Online publication date: 4-Dec-2014
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Yeh CTseng WChen CLin YTsai YBi HLin YLin H(2014)Popular music representationMultimedia Tools and Applications10.1007/s11042-013-1687-273:3(2103-2128)Online publication date: 1-Dec-2014
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