Bibliograafia: diginootide teemaga haakuvaid publikatsioone




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Bibliograafia: diginootide teemaga haakuvaid publikatsioone




2012


  1. Damm, David and Fremerey, Christian and Thomas, Verena and Clausen, Michael and Kurth, Frank and Müller, Meinard. A Digital Library Framework for Heterogeneous Music Collections – from Document Acquisition to Cross-Modal Interaction. International Journal on Digital Libraries: Special Issue on Digital Music Libraries, 2012. 20 p. (to appear)

2011


  1. Arora, Nitin. MXMLiszt: a preliminary MusicXML digital library platform built on available open-source technologiesOCLC Systems & Services, 27 (4), 2011, pp. 298-316. / Describes the genesis and structural components for an open-source MusicXML digital library platform. /

  2. Stewart, Darin. XML for Music. Electronic Musician, Digital Edition (13 Oct 2011) http://www.emusician.com/gear/0769/xml-for-music/140024 / Markup for Music. Who's Doing What? Beyond Notation. /

  3. Hankinson, Andrew and Roland, Perry and Fujinaga, Ichiro. The Music Encoding Initiative as a Document-Encoding Framework. In Proceedings of ISMIR 2011: 12th International Society for Music Information Retrieval Conference, October 24-28, 2011, Miami, 293-298. / Introduces MEI as a document-encoding framework. It can be extended to encode new types of notation, eliminating the need for creating specialized and potentially incompatible notation encoding standards. /

  4. Raphael, Christopher and Wang, Jingya. New Approaches to Optical Music Recognition. In Proceedings of ISMIR 2011: 12th International Society for Music Information Retrieval Conference, October 24-28, 2011, Miami, 305-310. / Beginnings of a new system for optical music recognition (OMR), aimed toward the score images of the InternationalMusic Score Library Project (IMSLP). /

  5. Viro, Vladimir. Peachnote: Music Score Search and Analysis Platform. In Proceedings of ISMIR 2011: 12th International Society for Music Information Retrieval Conference, October 24-28, 2011, Miami, 359-362. / Presents the first result – the Music Ngram Viewer and search engine, an analog of Google Books Ngram Viewer and Google Books search formusic scores. /

  6. Cuthbert, Michael Scott and Ariza, Christopher and Friedland, Lisa. Feature Extraction and Machine Learning on Symbolic Music using the music21 Toolkit. In Proceedings of ISMIR 2011: 12th International Society for Music Information Retrieval Conference, October 24-28, 2011, Miami, 387-392. / Describes the “feature” capabilities of music21, a general-purpose, open source toolkit for analyzing, searching, and transforming symbolic music data. Combines music21 with the data mining toolkits Orange and Weka. /

  7. Anonüümne. eMusicStand: an Intelligent Music Stand for Students and Professional Soloists, Ensamble and Orchestra Players. Apr 24, 2011. / Adding Artificial Intelligence techniques. / …………… http://armisael.silix.org/wp-content/uploads/2011/04/parmesan2011emusicstand.pdf

  8. Jiang, Nanzhu and Grosche, Peter and Konz, Verena and Müller, Meinard [ http://www.mpi-inf.mpg.de/~mmueller/index_publications.html]. Analyzing chroma feature types for automated chord recognition. Proceedings of the 42nd AES Conference, 2011. [pdf] / To automatically extract chord labels directly from the given audio data. Analyzes the role of the feature extraction step within the recognition pipeline of various chord recognition procedures based on template matching strategies and hidden Markov models. /

  9. Ewert, Sebastian and Müller, Meinard. Estimating note intensities in music recordings. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2011. [pdf]. / Automated methods for estimating note intensities in music recordings given a MIDI file (representing the score) and an audio recording (representing an interpretation) of a piece of music. /

  10. Müller, Meinard. New developments in music information retrieval. Proc. of the 42nd AES Conference, 2011. [pdf] / Gives an overview of new developments in the Music Information Retrieval (MIR) field with a focus on content-based music analysis tasks including audio retrieval, music synchronization, structure analysis, and performance analysis. /

  11. Müller, Meinard and Konz, Verena and Jiang, Nanzhu and Zuo, Zhe A multi-perspective user interface for music signal analysis. Proc. of the International Computer Music Conference (ICMC), pp. 205-211, 2011. [pdf] / Introduces various novel functionalities for a user interface that opens up new possibilities for viewing, comparing, interacting, and evaluating analysis results within a multi-perspective framework and bridges the gap between signal processing and music sciences. Exploits the fact that a given piece of music may have multiple, closely-related sources of information including different audio recordings and score-like MIDI representations. /

  12. Müller, Meinard and Konz, Verena. Automatisierte Methoden zur Unter­stützung der Interpretationsforschung. Klang und Begriff, vol. 4, pp. 1-12. Schott Verlag, 2011. [pdf] / Aktuelle Entwicklungen der automatisierten Musikverarbeitung aus Sicht der Informatik diskutiert. Insbesondere soll aufgezeigt werden, welche Werkzeuge die Informatik den Musikwissenschaften für die Interpretationsforschung zur Verfügung stellen kann und wo automatisierte Methoden an ihre Grenzen stoßen. /



2010





  1. Arora, Nitin. Beyond Images: Encoding Music for Access and Retrieval. University of Alabama research paper for Master's in Library and Information Science, Spring 2010. / Pro­vides a brief overview of ASCII and XML-based Digital SMR and the possibilities they present libraries in terms of access and retrieval. Discusses possibilities using MusicXML within the context of a prototypical Internet-based MusicXML access and retrieval platform MXMLiszt. /

  2. Kainhofer, Reinhold. A MusicXML Test Suite and a Discussion of Issues in MusicXML 2.0. In Proceedings of Linux Audio Conference 2010 (Utrecht, Netherlands, May 1-4, 2010). / Presents an extensive suite of MusicXML unit tests. The test suite consisting of more than 120 MusicXML test files, each checking one particular aspect of the MusicXML specification. Several shortcomings in the MusicXML specification detected. The obstacles encountered when trying to convert MusicXML data files to the LilyPond format discussed. /

  3. Hosken, Dan. An Introduction to Music Technology. New York: Routledge, 2010, 400 pages. / Overview of the essential elements of music technology for today's musician. Provides music students with the background necessary to apply technology in their creating, teaching, and performing. Five topics that underlie the hardware and software in use today: sound, audio, MIDI, synthesis and sampling, and computer notation and computer-assisted instruction. /

  4. Cuthbert, Michael Scott and Ariza, Christopher. music21: A Toolkit for Computer-Aided Musicology and Symbolic Music Data. In Proceedings of ISMIR 2010 11th International Conference on Music Information Retrieval (Utrecht, Netherlands, August 9-13, 2010), pp. 637-642. / An object-oriented toolkit for analyzing, searching, and transforming music in scorebased forms. Allows musicians and researchers to write simple scripts and reuse them in other projects. /

  5. Ariza, Christopher and Cuthbert, Michael Scott. Modeling Beats, Accents, Beams, and Time Signatures Hierarchically with music21 Meter Objects. In Proceedings of the 2010 International Computer Music Conference (New York, June 1-5, 2010). / The music21 TimeSignature object represents meters hierarchically, through independent display, beam, beat, and accent attributes capable of unlimited partitioning and nesting. This model, designed for applications in computer-aided musicology, accommodates any variety of compound, complex, or additive meters, can report beatposition and accent levels, and can algorithmically perform multi-level beaming or various types of metrical analysis. As part of the music21 Python toolkit, the meter module can read input from Humdrum and MusicXML and output to MusicXML and Lilypond. /

  6. Fremerey, Christian. Automatic Organization of Digital Music Documents – Sheet Music and Audio. PhD thesis. Bonn, Mai 2010, 172 p. http://hss.ulb.uni-bonn.de/2010/2242/2242-1.pdf / Presents work towards automatic organization and synchronization of scanned sheet music and digitized audio recordings in the scenario of a digital music library. /

  7. Fremerey, Christian and Müller, Meinard and Clausen, Michael. Handling Repeats and Jumps in Score-Performance Synchronization. In Proceedings of ISMIR 2010 11th International Conference on Music Information Retrieval (Utrecht,Netherlands,August9-13,2010),pp.243-248. http://ismir2010.ismir.net/proceedings/ismir2010-43.pdf/Models formally the task of score-performance synchronization. /

  8. Müller, Meinard and Konz, Verena and Clausen, Michael and Ewert, Sebastian and Fremerey, Christian. A Multimodal Way of Experiencing and Exploring Music. Interdisciplinary Science Reviews, Vol. 35 No. 2, June, 2010, 138-153 http://www.mpi-inf.mpg.de/~mmueller/publications/2010_MuellerClausenKonzEwertFremerey_MusicSynchronization_ISR.pdf / Music synchronization, identifying and linking semantically corresponding events present in different versions of the same underlying musical work. Introduces music synchronization, shows how synchronization techniques can be integrated into novel user interfaces. /

2009


  1. Good, Michael. Using MusicXML 2.0 for Music Editorial Applications. In Digitale Edition zwischen Experiment und Standardisierung, P. Stadler and J. Veit, eds., Max Niemeyer, Tübingen, 2009, pp. 157-174. Beihefte zu editio 31. /Digiversiooni ei ole/

  2. Nielsen, Johan Sejr Brinch. Statistical Analysis of Musical Corpora. Bachelor's thesis, Dept. of Computer Science, University of Copenhagen, January 2009. / Investigates trends in musical complexity, specifically by computing the entropy of chord sequences. Mozart pieces (571 of 626) analysed. The result shows an increase in entropy over time in at least five categories (divertimentos and serenades, piano pieces, piano trios, string quartets and symphonies). And increase in entropy is also observed as the works grow larger. The framework can be reused or further expanded. The results show promise for entropy as a measure for musical complexity. /

  3. Ganseman, Joachim and Scheunders, Paul and D'haes, Wim. Using XML-Formatted Scores in Real-Time Applications. In Proceedings of ISMIR 2009 10th International Conference on Music Information Retrieval (Kobe, Japan, October 26-30, 2009), pp. 663-668. / Presents fast and scalable methods to access relevant data from music scores stored in an XML based notation format, with the explicit goal of using scores in real-time audio processing frameworks. Real-time accessing or traversing a score is often time-critical. It is shown that with some well chosen design choices and precomputation of the necessary data, runtime time-complexity of several key score manipulation operations can be reduced to a level that allows use in a real-time context. /

  4. Kirlin, Phillip B.. Using Harmonic and Melodic Analyses to Automate the Initial Stages of Schenkerian Analysis. In Proceedings of ISMIR 2009 10th International Conference on Music Information Retrieval (Kobe, Japan, October 26-30, 2009), pp. 423-428. / The first major step in producing a Schenkerian analysis, involves selecting notes from a given composition for the primary soprano and bass parts of the analysis. Presents an algorithm that uses harmonic and melodic analyses. /

  5. Nichols, Eric and Morris, Dan and Basu, Sumit and Raphael, Christopher. Relationships Between Lyrics and Melody in Popular Music. In Proceedings of ISMIR 2009 10th International Conference on Music Information Retrieval (Kobe, Japan, October 26-30, 2009), pp. 471-476. / Presents the results of an observational study on a large symbolic database of popular music; identifies several patterns in the relationship between lyrics and melody. /

  6. Lehmann, Andreas. Automatisiertes Motivsuchen in Musikwerken im MusicXML Format. Diplomarbeit, Humboldt-Universität zu Berlin, June 2009.

  7. Abe, Ryosuke and Han, Dongxing and Tamura, Naoyoshi and Gotoh, Toshiyuki. Building an Automated Analysis System to Support Proofreading Braille Music Notation. IEICE Transactions on Information and Systems (Japanese Edition), Vol. J92-D, No. 4, April 2009, pp. 480-490. / Braille music notation has been used as musical notation for the visually impaired. Proposed is a system to support Braille music notation translators who need verification of their transcription. The system is designed to translate Braille music notation into 5-line music scores in order to compare them with the originals. Precision and processing speed are improved in the proposed system by introducing a pre-phase to optimize data and by introducing a chart-parser for disambiguation. Also, the reproducibility of the musical sign is improved in the proposed system by using MusicXML. /
  8. Modern Methods for Musicology: prospects, proposals, realities. Crawford, Tim and Gibson, Lorna (Eds) Ashgate Publishing Co, 2009. / Raamat. / nn

  9. Fremerey, Christian and Clausen, Michael and Ewert, Sebastian and Müller, Meinard. Sheet music-audio identification ISMIR 2009 10th International Conference on Music Information Retrieval (Kobe, Japan, October 26-30, 2009), pp. 645-650. [pdf] / Given a query consisting of a sequence of bars from a sheet music representation, the task is to find corresponding sections within an audio interpretation of the same piece. Two approaches are proposed: a semi-automatic approach using synchronization and a fully automatic approach using matching technique. /




2008





  1. Capela, Artur and Cardoso, Jaime S. and Rebelo, Ana and Guedes, Carlos. Integrated Recognition System for Music Scores. In Proceedings of the 2008 International Computer Music Conference (Belfast, August 24-29, 2008). / Proposes a system which offers a complete solution for the preservation of our musical heritage. It includes an optical recognition engine integrated with an archiving system and an interface for searching, browsing and edition. The digitized scores are stored in MusicXML. An additional benefit of the automatic conversion of the music score to MusicXML is the possibility of encoding the manuscript score in MX format, an XML-base, multi-layered format for music representation. MX synchronizes several layers belonging to the description of a piece of music, e.g. an audio recording and score of the same piece. A system of this kind promotes the creation of a full corpus of music documents. This project will culminate in the creation of a repository of the handwritten scores, accessible online.The database will be available for enjoyment, educational and musicological purposes, thus preserving this corpus of music in an unprecedented way. A prototype has been implemented and is being used as a test platform for OMR algorithms. /

  2. Knopke, Ian. The PerlHumdrum and PerlLilypond Toolkits for Symbolic Music Information Retrieval. ISMIR 2008 147-152. / PerlHumdrum is an alternative toolkit for working with large numbers of Humdrum scores. It is a self-contained implementation, fully object-oriented, designed to easily facilitate analysis and processing of multiple humdrum files, and to answer common musicological questions across entire sets, collections of music, or even the entire output of single or multiple composers. Several extended capabilities are provided, such as translation of MIDI scores to Humdrum, provisions for constructing graphs, a graphical user interface for non-programmers, and the ability to generate complete scores or partial musical examples as standard musical notation using PerlLilypond. These tools are intended primarily for use by music theorists, computational musicologists, and Music Information Retrieval (MIR) researchers. /

  3. Ganseman, Joachim and Scheunders, Paul and D'haes, Wim. Using XQuery on MusicXML Databases for Musicological Analysis. In Proceedings of ISMIR 2008 9th International Conference on Music Information Retrieval (Philadelphia, September 14-18, 2008), pp. 427-432. / When storing a large set of XML-encoded scores in an XML database, XQuery can be used to retrieve information from this database. Some small practical examples of such large scale analysis are given. The Wikifonia lead sheet database and the eXist XQuery engine are used, demonstrating the feasibility of automated musicological analysis on digital score libraries. /

  4. Szwoch, Mariusz. Using MusicXML to Evaluate Accuracy of OMR Systems. In Diagrammatic Representation and Inference: Proceedings of Diagrams 2008 (Herrsching, Germany, September 19-21, 2008), Springer-Verlag, Berlin, pp. 419-422. Lecture Notes in Computer Science, Vol. 5223. / Proposes a methodology for automatic accuracy evaluation in optical music recognition (OMR) applications. Assumes use of ground truth images together with digital music scores describing their content. The automatic evaluation algorithm measures differences between the tested score and the reference one, both stored in MusicXML format. Some preliminary test results of this approach are presented based on the algorithm’s implementation in OMR Guido application. /

  5. Gotoh, Toshiyuki and Minamikawa-Tachino, Reiko and Tamura, Naoyoshi. A Web-Based Braille Translation for Digital Music Scores. In Proceedings of ASSETS 2008 10th International ACM SIGACCESS Conference on Computers and Accessibility (Halifax, October 13-15, 2008), pp. 259-260. / Proposes computer environment called BrailleMUSE that is a free Braille music translation server on the Internet. The BrailleMUSE has a mirror page to a music site showing about 4,000 digital scores. The system provided Braille scores by working together with the music site. It is suggested that the system is suitable for practical use. / PDF – tasuline.

  6. Kirlin, Phillip B. and Utgoff, Paul E. A framework for automated Schenkerian analysis. In Proceedings of the Ninth International Conference on Music Information Retrieval, pages 363–368, Philadelphia, September 2008. / In Schenkerian analysis, one seeks to find structural dependences among the notes of a composition and organize these dependences into a coherent hierarchy that illustrates the function of every note. This type of analysis reveals multiple levels of structure in a composition by constructing a series of simplifications of a piece showing various elaborations and prolongations. A framework is presented that uses a state-space search formalism. It includes multiple interacting components, including modules for various preliminary analyses (harmonic, melodic, rhythmic, and cadential), identifying and performing reductions, and locating pieces of the Ursatz. Illustrated on an excerpt from a Schubert piano composition. /……………………………………………………….. http://ismir2008.ismir.net/papers/ISMIR2008_229.pdf

  7. Kuzmich, John, Jr. Integrating Power-User Applications: Part 2 of 2. School Band and Orchestra, April 2008, pp. 40-45. / Populaarne tutvustus. /

  8. Kuzmich, John, Jr. Music Tech Integration with Power-User Applications: Part 1 of 2. School Band and Orchestra, March 2008. / Ei leia /

  9. Bullen, Andrew H. Bringing Sheet Music to Life: My Experiences with OMR. Code{4}lib Journal. Issue 3, 2008-06-23 http://journal.code4lib.org/articles/84/ / Describes the process of digitizing sheet music celebrating Pullman porters and rail travel from the 1870s-1920s. The process involves 1) digitizing sheet music, 2) running the digitized sheet music through an Optical Musical Recognition (OMR) software package, 3)cleaning up the resulting file, 4) converting it into an .mp3/MIDI file, and 5) tweaking it to use the voices/instruments of a music editing software program. The pros and cons of some popular OMR programs are discussed. /

  10. Williams, David B. and Webster, Peter R. Experiencing Music Technology, Updated Third Edition. Schirmer Books, 2008. / One of the leading college textbooks for music technology courses. MusicXML is discussed in Module 19 on Coding Systems for Music Notation and Performance. / Raamat.

2007


  1. Clemmons, B. The Dictionary of North American Hymnology: Creating the Twenty-First Century Index of North American Hymnody. Choral Journal,  47 (9), March 2007, pp. 75-78.

  2. Vigliante, Raffaele. MusicXML: An XML Based Approach to Automatic Musicological Analysis. In Proceedings of Digital Humanities 2007 (Urbana-Champaign, IL, June 4-8, 2007), pp. 235-237. / Teksti ei leia./

  3. Barrett, Douglas G. and Winter, Michael and Wulfson, Harris. Automatic Notation Generators. In Proceedings of the 2007 International Computer Music Conference (Copenhagen, August 27-31, 2007). / Presents various custom software tools called Automatic Notation Generators (ANG’s) developed by the authors to aid in the creation of algorithmic instrumental compositions. The unique possibilities afforded by ANG software are described, along with relevant examples of their compositional output. These avenues of exploration include: mappings of spectral data directly into notated music, the creation of software transcribers that enable users to generate multiple realizations of algorithmic compositions, and new types of spontaneous performance with live generated screen-based music notation. /

  4. Knopke, I. and Byrd, D. Towards Musicdiff: A Foundation for Improved Optical Music Recognition Using Multiple Recognizers. In Proceedings of ISMIR 2007 8th International Conference on Music Information Retrieval (Vienna, September 23-27, 2007), pp. 123-126. / Presents work towards a “musicdiff” program for comparing files representing different versions of the same piece, primarily in the context of comparing versions produced by different optical music recognition (OMR) programs. The basic methodology requires several stages: documents must be scanned and submitted to several OMR programs, programs whose strengths and weaknesses have previously been evaluated in detail. Discusses techniques implemented for normalization, alignment and rudimentary error correction. A visualization tool for comparing multiple versions on a measure-by-measure basis is described. /

  5. Goto, D., Gotoh, T., Minamikawa-Tachino, R. and Tamura, N. A Transcription System from MusicXML Format to Braille Music Notation. EURASIP Journal on Advances in Signal Processing, vol. 2007, Article ID 42498, 9 pages, 2007. / Ei leia. /

  6. Mullins, Keith and Cahill, Margaret. Importing MusicXML files into Max/MSP. Technical Report UL-CSIS-07-01, University of Limerick, 2007. / A Max/MSP external object is created to allow musical scores to be imported into Max/MSP patches. This report details the programming of the object in Java and the parsing of the MusicXML file. /

  7. Psenicka, David. FOMUS, a Music Notation Software Package for Computer Music Composers. In Proceedings of the International Computer Music Conference. 2007. San Francisco: International Computer Music Association. 75-78. / A software tool that facilitates the process of representing algorithmically generated data as musical notation. It does this by spelling notes, quantizing offsets and durations, laying out information into voices and staves and making many other decisions such as where to include clef changes or octave transposition markings. A computer music composer using this software can work while being less concerned with the tedious difficulties involved in importing data into a graphical notation program. Information such as dynamic markings and articulations can also be generated and made to appear in the score. The program is written in Lisp, interfaces with Common Music and outputs files suitable for rendering with LilyPond or CMN or importing into a program that reads MusicXML format files such as Finale or Sibelius./ http://quod.lib.umich.edu/cgi/p/pod/dod-idx?c=icmc;idno=bbp2372.2006.018

  8. Marsden, Alan. Automatic derivation of musical structure: A tool for research on Schenkerian analysis. In Proceedings of the Eighth International Conference on Music Information Retrieval, pages 55–58, 2007. Extended Version. http://www.lancs.ac.uk/staff/marsdena/research/schenker/ExtendedVersion.pdf / Software has been developed which derives a tractable ‘matrix’ of possibilities from a musical surface (i.e., MIDI-like note-time information). The matrix is somewhat like the intermediate results of a dynamic-programming algorithm, and in a similar way it is possible to extract a particular structural analysis from the matrix by following the appropriate path from the top level to the surface. /

  9. Roland, Perry and Downie, Stephen J. Recent Developments in the Music Encoding Initiative Project: Enhancing DigitalMusicology and Scholarship. Proc. 19th Joint International Conference of the Association for Computers and the Humanities and the Association for Literaryand Linguistic Computing (Digital Humanities 2007), pp. 186–189, 2007. / Ei leia. /

  10. Baggi, Denis and Haus, Goffredo. The Concept of Interactive Music: the New Standard IEEE P1599 / MX,” Proc. 2nd International Conference on Semantic and Digital Media Technologies, SAMT 2007, pp. 185–195. / For at least forty centuries in all cultures, music has used symbols to represent its contents and give hints for its performance. This standard is the continuation of this tradition with its use of human and machine readable symbols using the XML language. The article illustrates the possibilities offered by the new standard IEEE P1599, locally known as project MX, through a few applications meant to show its flexibility and its role as enabling technology. /

  11. Ludovico, Luca A. Outline of the MX standard. Proc. 2nd International Conference on Semantic and Digital Media Technologies, SAMT 2007, pp. 196-199. http://www.ludovico.net/download/papers/SAMT2007.pdf / MX is a new XML-based format to describe comprehensively heterogeneous music contents. In a single MX file, music symbols, printed scores, audio tracks, computer-driven performances, catalogue metadata, text and graphic contents related to a single music piece are linked and mutually synchronised within the same framework. Heterogeneous contents are organised in a multilayered structure that supports different encoding formats and a number of digital objects for each layer. /

  12. Stadler, P. Digitale Edition zwischen Experiment und Standardisierung: Musik Text Codie­rung (Beihefter Zu Editio) (German and English Edition). Niemeyer, 2009. Proceedings of the Digital Editing Between Experiment and Standardization symposium held in 2007 in Paderborn, Germany. / Using MusicXML 2.0 for Music Editorial Applications describes how different MusicXML 2.0 features can be used in preparing digital critical editions. / Raamat.

  13. Müller, Meinard. Information Retrieval for Music and Motion. Springer, 2007, XVI, 318 p. [link]. / Details concepts and algorithms for robust and efficient information retrieval for waveform-based music data and human motion data. Combines elements from information science, digital signal processing, audio engineering, musicology, and computer graphics. /

2006





  1. Baratè, Adriano and Haus, Goffredo and Ludovico, Luca A. An XML-Based Format for Advanced Music Fruition in Proceedings of the Third Sound and Music Computing Conference, 2006, pp.141–147. / Points out the basic concepts of proposed XML encoding and presenting the process required to create rich multimedia descriptions of a music piece in MX format. /

  2. Good, Michael. Lessons from the Adoption of MusicXML as an Interchange Standard. In XML 2006 Conference Proceedings (Boston, MA, December 5-7, 2006). /An introduction to music application formats, symbolic formats using xml. Lessons to apply usability techniques to xml language design, develop the format together with the software, support a market leader early, market to other software developers, give format developers good support, avoid overhead. Presentation slides are available. /

  3. Good, Michael. MusicXML in Commercial Applications. In Music Analysis East and West, W. B. Hewlett and E. Selfridge-Field, eds., MIT Press, Cambridge, MA, 2006, pp. 9-20. Computing in Musicology 14. / Recent features and implementations of MusicXML 1.1 (May 2005). Current uses include the import and export of music notation files, the acquisition of scanned images of music, the support of digital music stands, and various applications in music education and research. As of November 2005, fifty applications have shipped or announced support for the MusicXML format, making it the first widely adopted format for symbolic music representation since MIDI. Future directions, including the evolution of MusicXML from an interchange format to a distribution format./

  4. Cunningham, Stuart and Gebert, Nicole and Picking, Rich and Grout Vic. Web-based music notation editing. In Proc. IADIS Int. Conf. on WWW/Internet 2006. Murcia, Spain, October 5-8, 2006. / A novel web-based application, provisionally titled MusFlash, has been developed to maximise the features of the MusicXML notation format. MusicXML is mainly supported in commercial products. MusicXML is a very Internet-friendly format, although the applications of this when used in platform specific commercial software are limited. A Flash-based web interface has been developed which not only provides a free, platform-independent MusicXML player and editor, but allows music composers to jointly share and co-author musical score. Presents the rationale and features of the application. /…………………………………… http://www.glyndwr.ac.uk/cunninghams/research/web_notation.pdf

  5. Selfridge-Field, Eleanor. XML Applications in Music Scholarship. In Music Analysis East and West, W. B. Hewlett and E. Selfridge-Field, eds., MIT Press, Cambridge, MA, 2006, pp. 21-40. Computing in Musicology 14. / Ei leia. /

  6. Dehghani, Morteza and Lovett, Andrew M. Efficient Genre Classification Using Qualitative Representations. In Proceedings of ISMIR 2006 7th International Conference on Music Information Retrieval (Victoria, Canada, October 8-12, 2006), pp. 353-354. / A system that can compute a qualitative representation of music from high-level features extracted from MusicXML files. Uses two cognitively motivated computational models called SME and SEQL to build generalizations of musical genres from these representations. Categorizes novel music pieces according to the generalizations. Demonstrates the feasibility of the system with training sets much smaller than those used in previous systems. /

  7. Chiu, Ai-Ti and Hsu, Jia-Lien. An Automaton-Based Filtering System for Streaming MusicXML. In Proceedings of the 2006 International Conference on Semantic Web & Web Services (Las Vegas, June 26-29, 2006), pp. 177-178. / Field: music information retrieval. Precise and efficient query/filter system against streaming MusicXML. User queries are specified in XPath expressions. Three key issues investigated: query notes of crossing measures, polyphonic music query, and the order of query notes. Proposes an automaton-based method to efficiently resolve user queries against streaming MusicXML. /



2005


  1. Alan Marsden. Generative structural representation of tonal music. Journal of New Music Research, 34(4):409–428, December 2005. http://www.lancs.ac.uk/staff/marsdena/publications/JNMR05forEprints.pdf

  2. Bellini, Pierfrancesco and Nesi, Paolo and Zoia, Giorgio. Symbolic music representation in MPEG,” IEEE Multimedia , Vol. 12, No. 4, pp. 42–49, 2005. Abstract: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1524887

  3. Bellmann, Héctor. About the Determination of Key of a Musical Excerpt. In Proceedings of the Third International Symposium on Computer Music Modeling and Retrieval (Pisa, September 26-28, 2005), Springer-Verlag, Heidelberg, pp. 76-91. Lecture Notes in Computer Science, Vol. 3902.



2004


  1. Kuipers, Gilbert, and Good, Michael. Using XML Technologies in Music. In Proceedings 37th Midwest Instruction and Computing Symposium (Morris, MN, April 16-17, 2004). / MusicXML: introduction, present status, future directions. /

  2. Cook, Nicholas. Computational and Comparative Musicology. In Empirical Musicology: Aims, Methods, Prospects. N. Cook and E. Clarke, eds. New York: Oxford University Press. 2004. 103-126. http://cf.hum.uva.nl/mmm/new-directions/Cook-computational.pdf

  3. Wang, Ye and Kan, Min-Yen and Nwe, Tin Lay and Shenoy, Nwe Arun and

  4. Yin, Jun: LyricAlly: Automatic Synchronization of Acoustic Musical Signals and Textual Lyrics. ProcACM Multimedia, Oct 2004. http://www.comp.nus.edu.sg/~kanmy/papers/p1568934817-wang.pdf

  5. Li, Tao and Ogihara, Mitsunori: Music artist style identification by semi-supervised learning from both lyrics and content. Proc ACM Multimedia, Oct 2004. http://users.cis.fiu.edu/~taoli/pub/acm-mm-p364-li.pdf

  6. Baird, Kevin C. Generating Music Notation in Real Time. Linux Journal, No. 128, December 2004, pp. 72-76.

  7. Kaji, Katsuhiko and Nagao, Katashi. MiXA: A Musical Annotation System. Demo paper from 3rd International Semantic Web Conference (Hiroshima, November 7-11, 2004).

  8. Didkovsky, Nick. Java Music Specification Language, V103 Update. In Proceedings of the 2004 International Computer Music Conference (Miami, November 1-6, 2004).

  9. Stevens, Al. Band-In-A-Box, Finale, & MusicXML. Dr. Dobb's Journal, 29 (9), September 2004, pp. 52-56. / How to convert Band-In-A-Box song files into Finale notation files by using MusicXML as a porting medium. /

  10. Kahlisch, Thomas and Leopold, Matthias and Waldvogel, Christian. DaCapo, A Project on Transforming Ink Printed to Braille Notes Semi-Automatically: The Project's Current State. In Proceedings of the 9th International Conference on Computers Helping People with Special Needs (Paris, July 7-9, 2004), Springer-Verlag, Heidelberg, pp. 224-227. Lecture Notes in Computer Science, Vol. 3118.

  11. Schnieders, Ralf. Von der Quelle zum (Daten-) Fluss: Neue Medien in der Editionswissenschaft. Das Orchester, January 2004. / Täisteksti ei ole. /

2003


  1. Nienhuys, Han-Wen. and Nieuwenhuizen Jan. LilyPond, a system for automated music engraving. Proceedings of the XIV Colloquium on Musical Informatics (XIV CIM 2003), Firenze, Italy, May 8-9-10, 2003 http://www.doctornerve.org/nerve/pages/icmc2004/JMSLUpdate.pdf

  2. Good, Michael and Actor, Geri. Using MusicXML for File Interchange. In Proceedings Third International Conference on WEB Delivering of Music (Leeds, UK, September 15-17, 2003), IEEE Press, Los Alamitos, CA, p. 153. / The MusicXML format is designed to be a universal translator for programs that understand common Western musical notation. Progress towards this goal – over a dozen programs supporting MusicXML as of June 2003. Some of the ways that MusicXML has been used for file interchange. MusicXML support status, breaking the scanning-notation barrier, Finale access. /

  3. Stewart, Darin. XML for Music: A Markup Language That Breaks Down Musical Barriers. Electronic Musician, 19 (13), December 2003, pp. 58-64. / Ei leia. /

  4. Hirata, Keiji and Noike, Kenzi and Katayose, Haruhiro. Proposal for a Performance Data Format. In Working Notes of IJCAI-03 Workshop on methods for automatic music performance and their applications in a public rendering contest (Acapulco, August 11, 2003).

  5. Santana, Hugo and Dahia, Márcio and Lima, Ernesto and Ramalho, Geber. VExPat: An Analysis Tool for the Discovery of Musical Patterns. IX Simpósio Brasileiro de Computação Musical - Brazilian Symposium on Computer Music (Campinas, August 6-8, 2003).

  6. Kuzmich, John, Jr. MusicXML: Preserving Your Music Beyond MIDI Files. Jazz Education Journal, July 2003.

  7. Cunningham, Stuart. Music File Formats and Project XEMO. MSc thesis, University of Paisley, 2003.

2002


  1. Good, Michael. MusicXML in Practice: Issues in Translation and Analysis. In Proceedings First International Conference MAX 2002: Musical Application Using XML (Milan, September 19-20, 2002), pp. 47-54. / Introduces the key design concepts behind MusicXML, discusses some of the translation issues that have emerged in current commercial applications, introduces the use of MusicXML together with XML Query for music analysis and information retrieval applications. /

  2. Pardo, Bryan and Birmingham, William P.. Algorithms for chordal analysis. Computer Music Journal, 26(2):27–49, 2002. http://www.mitpressjournals.org/doi/abs/10.1162/014892602760137167

  3. Roland, Perry. The music encoding initiative (MEI). Proc. 1st International Conference on Musical Applications Using XML, pp. 55–59, 2002. http://xml.coverpages.org/MAX2002-PRoland.pdf

  4. Huron, David. Music information processing using the Humdrum toolkit: Concepts, examples, and lessons. Computer Music Journal, vol. 26, no. 2, pp. 11–26, 2002. http://www.mitpressjournals.org/doi/abs/10.1162/014892602760137158?journalCode=comj

  5. Clemmons, William (Bill). New Standards and Emerging Technologies: A Rhythm Tutor with MusicXML and Finale. Association for Technology in Music Instruction (ATMI) Annual Conference (Kansas City, September 26-2009, 2002). / Ei leia /

  6. Tremblay, Guy and Champagne, France. Automatic Marking of Musical Dictations By Applying the Edit Distance Algorithm On a Symbolic Music Representation. In Proceedings First International Conference MAX 2002: Musical Application Using XML (Milan, September 19-20, 2002), pp. 11-17.

  7. Veit, Joachim. Mediale Revolution? Perspektiven und Probleme neuer Formen der Musikedition. November 21, 2002.

2001


  1. Dumitrescu, Theodor. Corpus Mensurabilis Musice ‘Electronicum’: Toward a Flexible Electronic Representation of Music in Mensural Notation. Computing in Musicology 12, 2001: 3-18. / Ei leia. /

  2. Good, Michael. MusicXML: An Internet-Friendly Format for Sheet Music. In XML 2001 Conference Proceedings (Orlando, FL, December 9-14, 2001). / The need for a new music interchange format, MusicXML’s approach to music interchange, elements of MusicXML design, freedom of choice for music software developers. /

  3. Good, Michael. MusicXML for Notation and Analysis. In The Virtual Score: Representation, Retrieval, Restoration, W. B. Hewlett and E. Selfridge-Field, eds., MIT Press, Cambridge, MA, 2001, pp. 113-124. Computing in Musicology 12. / Representing common western musical notation from the seventeenth century onwards, including both classical and popular music. Supporting interchange between musical notation, performance, analysis, and retrieval applications. MusicXML is designed to be sufficient, not optimal, for these applications. A sample MusicXML encoding, DTD examples, MusicXML analysis examples. /

  4. Castan, Gerd, Good, Michael, and Roland, Perry. Extensible Markup Language (XML) for Music Applications: An Introduction. In The Virtual Score: Representation, Retrieval, Restoration, W. B. Hewlett and E. Selfridge-Field, eds., MIT Press, Cambridge, MA, 2001, pp. 95-102. Computing in Musicology 12. / What is XML? XML and previous interchange initiatives, design principles of XML (Syntax: elements and attributes, semantics, definition of the semantics), XML for conventional music notation (expressing hierarchies, grouping by common values, grouping by common references), XML as a comprehensive data model. /

  5. Temperley, David. The Cognition of Basic Musical Structures. MIT Press, Cambridge, Massachusetts, 2001. …………………………………………… http://mitpress.mit.edu/catalog/item/default.asp?tid=8586&ttype=2

  6. Bellini, Pierfrancesco and Nesi, Paolo. WEDELMUSIC format: an XML music notation format for emerging applications,” Proc. 1st International Conference on Web Delivering of Music (WEDELMUSIC 2001), pp. 79–86, http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=990161&tag=1

  7. Choudhury, Sayeed G. and DiLauro, Tim and Droettboom, Michael and Fujinaga, Ichiro and MacMillan, Karl. Strike Up the Score. Deriving Searchable and Playable Digital Formats from Sheet Music. D-Lib Magazine February 2001 Volume 7 Number 2 http://www.dlib.org/dlib/february01/choudhury/02choudhury.html / Optical music recognition is a critical component for building strong online collections that include musical scores. Without it, collection managers would have to either limit end-user searching to descriptive metadata or bear the expense of manually transcribing and encoding their collections to provide music searching, lyric searching, and online playback. Though critical, OMR is only one of the tools that will be integrated into the Levy II workflow management system. A companion paper discusses the process for project planning, the automated name authority control tool, and the overall framework for the workflow management system itself. /

  8. Droettboom, Michael and Fujinaga, Ichiro. Interpreting the semantics of music notation using an extensible and object-oriented system. (? Published in Proceedings of the 9th Python Conference, 2001). http://droettboom.com/papers/python-paper.pdf

  9. Bainbridge, David and Bell, Tim. The Challenge of Optical Music Recognition. Computers and the Humanities 35: 95–121, 2001. http://www.informatics.indiana.edu/donbyrd/N560Site-Fall06/B%2BB_ChallengeOfOMR.pdf

2000


  1. Good, Michael. Representing Music Using XML. In Proceedings of ISMIR 2000 International Symposium on Music Information Retrieval (Plymouth, MA, October 23-25, 2000). / A poster session where MusicXML 0.1 was unveiled in public for the first time. /

  2. Heijink, Hank and Desain, Peter and Honing, Henkjan and Windsor, Luke. Make Me a Match: an evaluation of different approaches to Score-Performance Matching. Computer Music Journal, Vol. 24, No. 1, pp. 43–56, 2000. http://www.mitpressjournals.org/doi/abs/10.1162/014892600559173

  3. Choudhury, Sayeed G. and DiLauro, Tim and Droettboom, Michael and Fujinaga, Ichiro and Harrington, Brian and MacMillan, Karl. Optical music recognition system within a large-scale digitization project. ISMIR 2000 Conference. <http://ciir.cs.umass.edu/music2000/papers/choudhury_paper.pdf>

1997


  1. Huron, David. Humdrum and Kern: SelectiveFeature Encoding. In Beyond MIDI: the Handbook of Musical Codes. E. Selfridge-Field, ed. Cambrdige: MIT Press 1997. 375-401. http://books.google.ee/books?id=Xm3J9DG9EFcC&pg=PA375&lpg=PA375&dq=%22Humdrum+and+Kern:+Selective+Feature+Encoding%22&source=bl&ots=bIsJRItdhs&sig=nVtm-4fYZ8jpdY_TOAro6eh1EKc&hl=et&sa=X&ei=fZ0mT8bjLqfC0QW9-ITOCg&ved=0CCcQ6AEwAQ#v=onepage&q=%22Humdrum%20and%20Kern%3A%20Selective%20Feature%20Encoding%22&f=false

  2. Hoos, Holger H. and Hamel, Keith A. 1997. The GUIDO music notation format version 1.0, Specification Part 1: Basic GUIDO. Technical Report TI 20/97, Technische Universität Darmstadt. <http://www.informatik.tudarmstadt.de/AFS/GUIDO/docu/spec1.htm>

1996


  1. Fujinaga, Ichiro. Exemplar-based learning in adaptive optical music recognition system. Proceedings of the International Computer Music Conference. 1996: 55-56.
  2. Fujinaga, Ichiro. Adaptive Optical Music Recognition. Ph.D. thesis, 1996. McGill University.

  3. Bainbridge, David and Bell, Tim. An extensible Optical Music Recognition system. Proc. of the Nineteenth Australasian Computer Science Conf. (Melbourne, 1996) 308-317. / Optical music recognition (OMR) is a form of structured document image analysis where symbols overlaid on the conventional five-line stave are isolated and identified so that the music can be played through a MIDI system, or edited in a music publishing system. Traditionally OMR systems have had recognition techniques hard-coded in software. This paper describes a system that has been designed to be extensible without the need to change the system's source code. Extensibility is achieved by providing tools for music recognition that are used to tailor the system to suit the type of music being recognised. The tools include a selection of methods for identifying staves and isolating objects from them, methods for describing and identifying primitive musical shapes, and a grammar for specifying the relationships between the shapes that are recognised. The system is flexible enough to work with different publishers' symbol sets, and even with different types of music notation, such as the square-note notation used in early music. /

1995





  1. Coüasnon, Bertrand and Brisset, Pascal and Stephan, Igor.  Using Logic Programming Languages For Optical Music Recognition. In The Third International Conference on the Practical Application of Prolog, pages 115-134, Paris, France, April 1995.

1994

  1. Coüasnon, Bertrand and Camillerapp, Jean. Using Grammars to Segment and Recognize Music Scores. In International Association for Pattern Recognition Workshop on Document Analysis Systems, pages 15-27, Kaiserslautern, Germany, October 1994.


  2. Selfridge-Field, Eleanor. Optical recognition of music notation: A survey of current work. Computing in Musicology: An International Directory of Applications, Vol. 9, 1994, pp. 109-145. / Ei leia. /



1993





  1. Smaill, A. and G. Wiggins, M. Harris. Hierarchical Music Representation for Analysis and Composition. Computers and the Humanities 27(1) 1993.: 7-17. / Ei leia. /

1992


  1. Balaban, Mira. Music Structures: Interleaving the Temporal and Hierarchical Aspects in Music. In Understanding Music with AI: Perspectives on Music Cognition. M. Balaban, K. Ebcioglu and O. E. Laske, eds. Cambridge: AAAI Press / MIT Press, 1992. 31-48. http://www.jstor.org/stable/3681334

  2. Fujinaga, Ichiro. An optical music recognition system that learns. In (Ed) Jacek Maitan (editor), Enabling Technologies for High-Bandwidth Applications, pages 210-217, SPIE 1785, 1992. / Ei leia. /

1991





  1. Bainbridge, David. Preliminary experiments in musical score recognition. BEng. thesis, Department of Computer Science, University. of Edinburgh, The Kings Buildings, Mayfield Road, Edinburgh, UK, June 1991. / Ei leia. /

1990





  1. Polansky, Larry and Burk, Phil and Rosenboom, David. HMSL (Hierarchical Music Specification Language): A Theoretical Overview.” Perspectives of New Music 28(1-2), 1990, 136-178. http://music.dartmouth.edu/~larry/published_articles/HMSLPNM.pdf

1989


  1. Carter, Nicholas Paul. Automatic Recognition of Printed Music in the Context of Electronic Publishing. PhD thesis. Depts. of Physics and Music University of Surrey, February 1989. /Toodud on teemakohane bibliograafia 1964 – 1988/


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