Editing Music information retrieval

{{Short description|Interdisciplinary science of getting data from music}}
{{Multiple issues|
{{technical|date=October 2012}}
{{More citations needed|date=May 2021}}
{{Original research|date=May 2021}}
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'''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.

== Applications ==
Music information retrieval is being used by businesses and academics to categorize, manipulate and even create music.

=== Music classification ===
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 music|classical]], [[jazz]], [[Rock music|rock]], etc. [[Emotion classification|Mood classification]], artist classification, instrument identification, and music tagging are also popular topics. 

=== Recommender systems ===
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 Radio|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 [[musical similarity|similarity in music]] are now beginning to form part of such systems.

=== Music source separation and instrument recognition ===
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===
Automatic [[Transcription (music)|music transcription]] is the process of converting an audio recording into symbolic notation, such as a score or a [[MIDI file#File formats|MIDI file]].<ref>A. Klapuri and M. Davy, editors. Signal Processing Methods for Music Transcription. Springer-Verlag, New York, 2006.</ref> This process involves several audio analysis tasks, which may include multi-pitch detection, [[Onset detection#Onset detection|onset detection]], duration estimation, instrument identification, and the extraction of [[harmonic]], [[Rhythm|rhythmic]] or [[Melody|melodic]] information. This task becomes more difficult with greater numbers of instruments and a greater [[Polyphony and monophony in instruments|polyphony level]].

===Music generation===
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==

===Data source===
[[Sheet music|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. [[Audio file format|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 tagging|social tags]] for music.

===Feature representation===
Analysis can often require some summarising,<ref>Eidenberger, Horst (2011). "Fundamental Media Understanding", atpress. {{ISBN|978-3-8423-7917-6}}.</ref> and for music (as with many other forms of data) this is achieved by [[feature extraction]], especially when the [[Computer audition|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|Mel-Frequency Cepstral Coefficient]] (MFCC) which is a measure of the [[timbre]] of a [[Musical composition|piece of music]]. Other features may be employed to represent the [[Tonality#Computational methods to determine the key|key]], [[Chord (music)|chords]], [[Harmony|harmonies]], [[melody]], main [[Pitch (music)|pitch]], [[beats per minute]] or rhythm in the piece. There are a number of available audio feature extraction tools<ref>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.</ref>  [https://www.ntnu.edu/documents/1001201110/1266017954/DAFx-15_submission_43_v2.pdf Available here]

===Statistics and machine learning===
*Computational methods for classification, clustering, and modelling — musical feature extraction for mono- and [[polyphonic]] music, similarity and [[pattern matching]], retrieval
* [[Formal methods]] and [[Database|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 [[information retrieval|retrieval]], [[multi-agent system]]s, distributed search)
*Software for music information retrieval — [[Semantic Web]] and musical digital objects, [[Intelligent agent|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==
*[[Human–computer interaction|Human-computer interaction]] and interfaces — [[Multimodal interaction|multi-modal interfaces]], [[user interface]]s and [[usability]], [[Mobile app|mobile applications]], user behavior
* Music perception, cognition, affect, and emotions — music [[similarity metric]]s, syntactical parameters, semantic parameters, musical forms, structures, styles and music annotation methodologies
* Music archives, libraries, and digital collections — music [[digital library|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 metrics

== Academic activity ==
* [[International Society for Music Information Retrieval|International 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|International Conference on Acoustics, Speech, and Signal Processing (ICASSP)]] is also a highly relevant venue.

== See also ==
{{div col|colwidth=22em}}
* [[Audio 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 computing]]
{{div col end}}

== References ==
{{Reflist}}
* Michael Fingerhut (2004). [http://mediatheque.ircam.fr/articles/textes/Fingerhut04b "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==
* [http://www.ismir.net/ International Society for Music Information Retrieval]
* [http://music-ir.org/ Music Information Retrieval research]
* [https://dx.doi.org/10.1561/1500000042 M. Schedl, E. Gómez and J. Urbano: Music Information Retrieval: Recent Developments and Applications]
* [https://ccrma.stanford.edu/wiki/MIR_workshop_2011 Intelligent Audio Systems: Foundations and Applications of Music Information Retrieval, introductory course at Stanford University's Center for Computer Research in Music and Acoustics]
* [http://biblio.ugent.be/record/470088 Micheline Lesaffre: Music Information Retrieval: Conceptual Framework, Annotation and User behavior.]
* [http://www.imagine-research.com/ Imagine Research: develops platform and software for MIR applications]
* [http://www.AudioContentAnalysis.org/ AudioContentAnalysis.org: MIR resources and matlab code ]
* [https://music-classification.github.io/tutorial Minz Won, Janne Spijkervet, and Keunwoo Choi: Tutorial - Music classification: Beyond Supervised Learning, Towards Real-world Applications]

===Example MIR applications===
* [http://www.musipedia.org/ Musipedia — A melody search engine that offers several modes of searching, including whistling, tapping, piano keyboard, and Parsons code.]
* [http://www.peachnote.com/ Peachnote — A melody search engine and n-gram viewer that searches through digitized music scores]

{{Computer audition}}
[[Category:Music information retrieval| ]]
[[Category:Music software]]