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Optical music recognition: state-of-the-art and open issues
- Ana Rebelo, Ichiro Fujinaga, F. Paszkiewicz, A. Marçal, C. Guedes, Jaime S. Cardoso
- Computer ScienceInternational Journal of Multimedia Information…
- 2 March 2012
An overview of the literature concerning the automatic analysis of images of printed and handwritten musical scores and a reference scheme for any researcher wanting to compare new OMR algorithms against well-known ones is presented.
jAudio: An Feature Extraction Library
jAudio is a new framework for feature extraction designed to eliminate the duplication of effort in calculating features from an audio signal and provides a unique method of handling multidimensional features and a new mechanism for dependency handling to prevent duplicate calculations.
Musical genre classification: Is it worth pursuing and how can it be improved?
A number of counterarguments that emphasize the importance of continuing research in automatic genre classification are presented and specific strategies for overcoming current performance limitations are discussed.
Automatic Genre Classification Using Large High-Level Musical Feature Sets
This paper presents a system that extracts 109 musical features from symbolic recordings (MIDI) and uses them to classify the recordings by genre and argues the importance of using high-level musical features, something that has been largely neglected in automatic classification systems to date in favour of low-level features.
A Comparative Study of Staff Removal Algorithms
- C. Dalitz, M. Droettboom, B. Pranzas, Ichiro Fujinaga
- Computer ScienceIEEE Transactions on Pattern Analysis and Machine…
- 1 May 2008
A quantitative comparison of different algorithms for the removal of stafflines from music images is presented and a new skeletonization-based approach is suggested.
Design and creation of a large-scale database of structural annotations
- Jordan B. L. Smith, J. Burgoyne, Ichiro Fujinaga, D. D. Roure, J. S. Downie
- Computer ScienceISMIR
The design and creation of an unprecedentedly large database of over 2400 structural annotations of nearly 1400 musical recordings is described, intended to be a test set for algorithms that will be used to analyze a much larger corpus of hundreds of thousands of recordings.
The Music Encoding Initiative as a Document-Encoding Framework
This paper introduces MEI as a document-encoding framework, and illustrates how it can be extended to encode new types of notation, eliminating the need for creating specialized and potentially incompatible notation encoding standards.
jSymbolic: A Feature Extractor for MIDI Files
A library of 160 high-level features is presented along with jSymbolic, a software package that extracts these features from MIDI files that can be used to automatically classify music or evaluate musical similarity.
An Expert Ground Truth Set for Audio Chord Recognition and Music Analysis
Working with a team of trained jazz musicians, time-aligned transcriptions of the harmony in more than a thousand songs selected randomly from the Billboard “Hot 100” chart in the United States between 1958 and 1991 are collected.