MIR is being used by businesses and academics to categorize, manipulate and even create music.
Music classificationedit
One of the classical MIR research topics is genre classification, which is categorizing music items into one of the pre-defined genres such as classical, jazz, rock, etc. Mood classification, artist classification, instrument identification, and music tagging are also popular topics.
Recommender systemsedit
Several recommender systems for music already exist, but surprisingly few are based upon MIR techniques, instead of making use of similarity between users or laborious data compilation. Pandora, for example, uses experts to tag the music with particular qualities such as "female singer" or "strong bassline". Many other systems find users whose listening history is similar and suggests unheard music to the users from their respective collections. MIR techniques for similarity in music are now beginning to form part of such systems.
Music source separation and instrument recognitionedit
Music source separation is about separating original signals from a mixture audio signal. Instrument recognition is about identifying the instruments involved in music. Various MIR systems have been developed that can separate music into its component tracks without access to the master copy. In this way, for example, karaoke tracks can be created from normal music tracks, though the process is not yet perfect owing to vocals occupying some of the same frequency space as the other instruments.
Automatic music transcriptionedit
Automatic music transcription is the process of converting an audio recording into symbolic notation, such as a score or a MIDI file.[1] This process involves several audio analysis tasks, which may include multi-pitch detection, onset detection, duration estimation, instrument identification, and the extraction of harmonic, rhythmic or melodic information. This task becomes more difficult with greater numbers of instruments and a greater polyphony level.
Music generationedit
The automatic generation of music is a goal held by many MIR researchers. Attempts have been made with limited success in terms of human appreciation of the results.
Methods usededit
Data sourceedit
Scores give a clear and logical description of music from which to work, but access to sheet music, whether digital or otherwise, is often impractical. MIDI music has also been used for similar reasons, but some data is lost in the conversion to MIDI from any other format, unless the music was written with the MIDI standards in mind, which is rare. Digital audio formats such as WAV, mp3, and ogg are used when the audio itself is part of the analysis. Lossy formats such as mp3 and ogg work well with the human ear but may be missing crucial data for study. Additionally some encodings create artifacts which could be misleading to any automatic analyser. Despite this the ubiquity of the mp3 has meant much research in the field involves these as the source material. Increasingly, metadata mined from the web is incorporated in MIR for a more rounded understanding of the music within its cultural context, and this recently consists of analysis of social tags for music.
Feature representationedit
Analysis can often require some summarising,[2] and for music (as with many other forms of data) this is achieved by feature extraction, especially when the audio content itself is analysed and machine learning is to be applied. The purpose is to reduce the sheer quantity of data down to a manageable set of values so that learning can be performed within a reasonable time-frame. One common feature extracted is the Mel-Frequency Cepstral Coefficient (MFCC) which is a measure of the timbre of a piece of music. Other features may be employed to represent the key, chords, harmonies, melody, main pitch, beats per minute or rhythm in the piece. There are a number of available audio feature extraction tools[3] Available here
Statistics and machine learningedit
Computational methods for classification, clustering, and modelling — musical feature extraction for mono- and polyphonic music, similarity and pattern matching, retrieval
Music analysis and knowledge representation — automatic summarization, citing, excerpting, downgrading, transformation, formal models of music, digital scores and representations, music indexing and metadata.
Music perception, cognition, affect, and emotions — music similarity metrics, syntactical parameters, semantic parameters, musical forms, structures, styles and music annotation methodologies
Music archives, libraries, and digital collections — music digital libraries, public access to musical archives, benchmarks and research databases
Sociology and Economy of music — music industry and use of MIR in the production, distribution, consumption chain, user profiling, validation, user needs and expectations, evaluation of music IR systems, building test collections, experimental design and metrics
^A. Klapuri and M. Davy, editors. Signal Processing Methods for Music Transcription. Springer-Verlag, New York, 2006.
^Eidenberger, Horst (2011). “Fundamental Media Understanding”, atpress. ISBN978-3-8423-7917-6.
^David Moffat, David Ronan, and Joshua D Reiss. "An Evaluation of Audio Feature Extraction Toolboxes". In Proceedings of the International Conference on Digital Audio Effects (DAFx), 2016.
Michael Fingerhut (2004). "Music Information Retrieval, or how to search for (and maybe find) music and do away with incipits", IAML-IASA Congress, Oslo (Norway), August 8–13, 2004.
External linksedit
International Society for Music Information Retrieval
Music Information Retrieval research
M. Schedl, E. Gómez and J. Urbano: Music Information Retrieval: Recent Developments and Applications
Intelligent Audio Systems: Foundations and Applications of Music Information Retrieval, introductory course at Stanford University's Center for Computer Research in Music and Acoustics
Micheline Lesaffre: Music Information Retrieval: Conceptual Framework, Annotation and User behavior.
Imagine Research: develops platform and software for MIR applications
AudioContentAnalysis.org: MIR resources and matlab code
Minz Won, Janne Spijkervet, and Keunwoo Choi: Tutorial - Music classification: Beyond Supervised Learning, Towards Real-world Applications
Example MIR applicationsedit
Musipedia — A melody search engine that offers several modes of searching, including whistling, tapping, piano keyboard, and Parsons code.
Peachnote — A melody search engine and n-gram viewer that searches through digitized music scores
January 01, 1970
music, information, retrieval, this, article, multiple, issues, please, help, improve, discuss, these, issues, talk, page, learn, when, remove, these, template, messages, this, article, technical, most, readers, understand, please, help, improve, make, underst. This article has multiple issues Please help improve it or discuss these issues on the talk page Learn how and when to remove these template messages This article may be too technical for most readers to understand Please help improve it to make it understandable to non experts without removing the technical details October 2012 Learn how and when to remove this message This article needs additional citations for verification Please help improve this article by adding citations to reliable sources Unsourced material may be challenged and removed Find sources Music information retrieval news newspapers books scholar JSTOR May 2021 Learn how and when to remove this message This article possibly contains original research Please improve it by verifying the claims made and adding inline citations Statements consisting only of original research should be removed May 2021 Learn how and when to remove this message Learn how and when to remove this message Music information retrieval MIR is the interdisciplinary science of retrieving information from music Those involved in MIR may have a background in academic musicology psychoacoustics psychology signal processing informatics machine learning optical music recognition computational intelligence or some combination of these Contents 1 Applications 1 1 Music classification 1 2 Recommender systems 1 3 Music source separation and instrument recognition 1 4 Automatic music transcription 1 5 Music generation 2 Methods used 2 1 Data source 2 2 Feature representation 2 3 Statistics and machine learning 3 Other issues 4 Academic activity 5 See also 6 References 7 External links 7 1 Example MIR applicationsApplications editMIR is being used by businesses and academics to categorize manipulate and even create music Music classification edit One of the classical MIR research topics is genre classification which is categorizing music items into one of the pre defined genres such as classical jazz rock etc Mood classification artist classification instrument identification and music tagging are also popular topics Recommender systems edit Several recommender systems for music already exist but surprisingly few are based upon MIR techniques instead of making use of similarity between users or laborious data compilation Pandora for example uses experts to tag the music with particular qualities such as female singer or strong bassline Many other systems find users whose listening history is similar and suggests unheard music to the users from their respective collections MIR techniques for similarity in music are now beginning to form part of such systems Music source separation and instrument recognition edit Music source separation is about separating original signals from a mixture audio signal Instrument recognition is about identifying the instruments involved in music Various MIR systems have been developed that can separate music into its component tracks without access to the master copy In this way for example karaoke tracks can be created from normal music tracks though the process is not yet perfect owing to vocals occupying some of the same frequency space as the other instruments Automatic music transcription edit Automatic music transcription is the process of converting an audio recording into symbolic notation such as a score or a MIDI file 1 This process involves several audio analysis tasks which may include multi pitch detection onset detection duration estimation instrument identification and the extraction of harmonic rhythmic or melodic information This task becomes more difficult with greater numbers of instruments and a greater polyphony level Music generation edit The automatic generation of music is a goal held by many MIR researchers Attempts have been made with limited success in terms of human appreciation of the results Methods used editData source edit Scores give a clear and logical description of music from which to work but access to sheet music whether digital or otherwise is often impractical MIDI music has also been used for similar reasons but some data is lost in the conversion to MIDI from any other format unless the music was written with the MIDI standards in mind which is rare Digital audio formats such as WAV mp3 and ogg are used when the audio itself is part of the analysis Lossy formats such as mp3 and ogg work well with the human ear but may be missing crucial data for study Additionally some encodings create artifacts which could be misleading to any automatic analyser Despite this the ubiquity of the mp3 has meant much research in the field involves these as the source material Increasingly metadata mined from the web is incorporated in MIR for a more rounded understanding of the music within its cultural context and this recently consists of analysis of social tags for music Feature representation edit Analysis can often require some summarising 2 and for music as with many other forms of data this is achieved by feature extraction especially when the audio content itself is analysed and machine learning is to be applied The purpose is to reduce the sheer quantity of data down to a manageable set of values so that learning can be performed within a reasonable time frame One common feature extracted is the Mel Frequency Cepstral Coefficient MFCC which is a measure of the timbre of a piece of music Other features may be employed to represent the key chords harmonies melody main pitch beats per minute or rhythm in the piece There are a number of available audio feature extraction tools 3 Available here Statistics and machine learning edit Computational methods for classification clustering and modelling musical feature extraction for mono and polyphonic music similarity and pattern matching retrieval Formal methods and databases applications of automated music identification and recognition such as score following automatic accompaniment routing and filtering for music and music queries query languages standards and other metadata or protocols for music information handling and retrieval multi agent systems distributed search Software for music information retrieval Semantic Web and musical digital objects intelligent agents collaborative software web based search and semantic retrieval query by humming Search by sound acoustic fingerprinting Music analysis and knowledge representation automatic summarization citing excerpting downgrading transformation formal models of music digital scores and representations music indexing and metadata Other issues editHuman computer interaction and interfaces multi modal interfaces user interfaces and usability mobile applications user behavior Music perception cognition affect and emotions music similarity metrics syntactical parameters semantic parameters musical forms structures styles and music annotation methodologies Music archives libraries and digital collections music digital libraries public access to musical archives benchmarks and research databases Intellectual property rights and music national and international copyright issues digital rights management identification and traceability Sociology and Economy of music music industry and use of MIR in the production distribution consumption chain user profiling validation user needs and expectations evaluation of music IR systems building test collections experimental design and metricsAcademic activity editInternational Society for Music Information Retrieval ISMIR conference is the top tier venue for music information retrieval research International Conference on Acoustics Speech and Signal Processing ICASSP is also a highly relevant venue See also editAudio search engine Audio mining A Dictionary of Musical Themes Digital rights management Digital signal processing Ethnomusicology List of music software Multimedia information retrieval Automatic content recognition Music notation Musicology Optical music recognition Parsons code Sound and music computingReferences edit A Klapuri and M Davy editors Signal Processing Methods for Music Transcription Springer Verlag New York 2006 Eidenberger Horst 2011 Fundamental Media Understanding atpress ISBN 978 3 8423 7917 6 David Moffat David Ronan and Joshua D Reiss An Evaluation of Audio Feature Extraction Toolboxes In Proceedings of the International Conference on Digital Audio Effects DAFx 2016 Michael Fingerhut 2004 Music Information Retrieval or how to search for and maybe find music and do away with incipits IAML IASA Congress Oslo Norway August 8 13 2004 External links editInternational Society for Music Information Retrieval Music Information Retrieval research M Schedl E Gomez and J Urbano Music Information Retrieval Recent Developments and Applications Intelligent Audio Systems Foundations and Applications of Music Information Retrieval introductory course at Stanford University s Center for Computer Research in Music and Acoustics Micheline Lesaffre Music Information Retrieval Conceptual Framework Annotation and User behavior Imagine Research develops platform and software for MIR applications AudioContentAnalysis org MIR resources and matlab code Minz Won Janne Spijkervet and Keunwoo Choi Tutorial Music classification Beyond Supervised Learning Towards Real world Applications Example MIR applications edit Musipedia A melody search engine that offers several modes of searching including whistling tapping piano keyboard and Parsons code Peachnote A melody search engine and n gram viewer that searches through digitized music scores Retrieved from https en wikipedia org w index php title Music information retrieval amp oldid 1189049395, wikipedia, wiki, book, books, library,
