Open Access
Development of the RWC Music Database
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TLDR
The RWC (Real World Computing) Music Database is introduced, a copyright-cleared music database that is available to researchers as a common foundation for research and has already been widely used.Abstract:
In this paper I introduce the RWC (Real World Computing) Music Database, a copyright-cleared music database (DB) that is available to researchers as a common foundation for research. Shared DBs are common in other research fields and have made significant contributions to progress in those fields. The field of music information processing, however, has lacked a common DB of musical pieces or a large-scale DB of musical instrument sounds. The RWC Music Database was therefore built as the world’s first large-scale music DB compiled specifically for research purposes. It contains six original component DBs: the Popular Music Database, Royalty-Free Music Database, Classical Music Database, Jazz Music Database, Music Genre Database, and Musical Instrument Sound Database. The DB has been distributed to researchers at a nominal cost to cover only duplication, shipping, and handling charges (i.e., it is practically free), and has already been widely used.read more
Citations
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Book ChapterDOI
Summary and Discussion
TL;DR: This chapter presents the summary and the discussion of this book and database, in which the UMA-Piano chord music Data Base is presented as an engineering point of view of harmony.
Journal ArticleDOI
A chorus section detection method for musical audio signals and its application to a music listening station
TL;DR: An application of the RefraiD method, a new music-playback interface for trial listening called SmartMusicKIOSK, which enables a listener to directly jump to and listen to the chorus section while viewing a graphical overview of the entire song structure.
Proceedings Article
Evaluation of Multiple-F0 Estimation and Tracking Systems.
TL;DR: This paper presents the systematic evaluations of over a dozen competing methods and algorithms for extracting the fundamental frequencies of pitched sound sources in polyphonic music.
Proceedings ArticleDOI
Non-negative matrix factorization based compensation of music for automatic speech recognition.
TL;DR: Non-negative matrix factorization based speech enhancement in robust automatic recognition of mixtures of speech and music is proposed and shown to produce a consistent, significant improvement on the recognition performance in the comparison with the baseline method.
Dissertation
Automatic annotation of musical audio for interactive applications
TL;DR: This work is interested in developing a robust layer for the automatic annotation of audio signals, to be used in various applications, from music search engines to interactive installations, and in various contexts, from embedded devices to audio content servers.
References
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Proceedings Article
RWC Music Database: Popular, Classical, and Jazz Music Databases
TL;DR: The design policy and specifications of the RWC Music Database are described, a music database (DB) that is available to researchers for common use and research purposes, which contains four original DBs: the Popular Music Database (100 pieces), Royalty-Free Music Database(15 pieces), Classical Music Database ($50 pieces), and Jazz Music Database (£50 pieces).
Proceedings Article
RWC Music Database: Music Genre Database and Musical Instrument Sound Database
TL;DR: The design policy and specifications of the RWC Music Database, a copyright-cleared music database compiled specifically for research purposes, are described and it is hoped that the DB will make a significant contribution to future advances in the field of music information processing.
Journal ArticleDOI
A sound source identification system for ensemble music based on template adaptation and music stream extraction
Kunio Kashino,Hiroshi Murase +1 more
TL;DR: An adaptive method for template matching that can cope with variability in musical sounds is proposed that is applicable to real performances of ensemble music and discusses musical context integration based on the Bayesian probabilistic networks.