Problems of music information retrieval in the real world

Byrd, Donald; Crawford, Tim

doi:10.1016/s0306-4573(01)00033-4

Cited by 90 publications

(66 citation statements)

References 23 publications

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“…Let S be a music sequence, where S = EBCDEHGABFJDEHGJEBCDEABFJ. Following the previously discussion, the repeating patterns of length 1, 2, 4 do exist, though RP [8]=∅, where RP[x] denotes the set of repeating patterns with length equal to x. To determine L (and the corresponding maximum-length repeating patterns), we consider that k l = 3, since 8 = 2 3 ; whereas k l−1 = 2, since 4 = 2 2 and RP [4] [5] contains only one such pattern).…”

Section: The Hlc Algorithm-our Baseline Competitorsupporting

confidence: 59%

“…Music is available in three basic representations: Audio, Time-stamped Events and Common Music Notation [8]. To achieve semantic, efficiency as well as to overcome data processing constraints, the previously mentioned basic representations require a mapping.…”

Section: Work In Music Databasesmentioning

confidence: 99%

“…In general, music is characterized by several features. Among them, pitch, rhythm, timbre, and dynamics are considered to be the most semantically important ones [8]. For western music in particular, the pitch carries the highest relative weight of information [8].…”

Section: Definitionsmentioning

confidence: 99%

“…Among them, pitch, rhythm, timbre, and dynamics are considered to be the most semantically important ones [8]. For western music in particular, the pitch carries the highest relative weight of information [8]. In general, rhythm cannot be overlooked, for easier presentation however, we focus on the pitch information.…”

Section: Definitionsmentioning

confidence: 99%

“…This is mostly evident during the initial steps, when the number of repeating patterns of relatively small lengths is very large, due to the anti-monotonicity property. 8 Therefore, a new approach is required that will avoid as much as possible the cost to examine (i.e., counting the frequency) intermediate patterns.…”

Section: Motivationmentioning

confidence: 99%

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Finding maximum-length repeating patterns in music databases

Karydis

Νανόπουλος

Manolopoulos

2006

Multimed Tools Appl

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This paper introduces the problem of discovering maximum-length repeating patterns in music objects. A novel algorithm is presented for the extraction of this kind of patterns from a melody music object. The proposed algorithm discovers all maximum-length repeating patterns using an "aggressive" accession during searching, by avoiding costly repetition frequency calculation and by examining as few as possible repeating patterns in order to reach the maximum-length repeating pattern(s). Detailed experimental results illustrate the significant performance gains due to the proposed algorithm, compared to an existing baseline algorithm.

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Section: The Hlc Algorithm-our Baseline Competitorsupporting

confidence: 59%

Section: Work In Music Databasesmentioning

confidence: 99%

Section: Definitionsmentioning

confidence: 99%

Section: Definitionsmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

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Finding maximum-length repeating patterns in music databases

Karydis

Νανόπουλος

Manolopoulos

2006

Multimed Tools Appl

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Music information retrieval

Downie

2003

Annual Review Info Sci & Tec

249

120

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Welcome friends and colleagues to the 2 nd Annual International Symposium on Music Information Retrieval -ISMIR 2001.Following on the heels of last year's groundbreaking inaugural conference, we're convening with colleagues this year at the beautiful campus of Indiana University, Bloomington. We hope the information exchange fostered by this conference will facilitate innovation and enhance collaboration in this dynamic area of research. This year's program is rich in content and variety. We are honored to have David Cope present this year's keynote address. The presentations by our four invited speakers, Roger Dannenberg, Jef Raskin, Youngmoo Kim, and Adam Lindsay provide added depth and breadth to an already dynamic and diverse program.This document includes the texts of the accepted papers along with the extended abstracts of the invited talks and poster presentations. All are also available on the ISMIR 2001Web site at: http:// ismir2001.indiana.edu/ As with last year, we were very encouraged by the number and quality of submissions. Response to our Call for Papers was remarkable. Selecting the twenty papers for presentation (out of 40 submissions) and the eighteen posters for exhibition was no easy task. I'd like to personally thank all those that gave of their time to help review submissions.Unending appreciation and thanks must be extended to the Program Committee: David Bainbridge (Program Chair), Gerry Bernbom, Donald Byrd, Tim Crawford, Jon Dunn, and Michael Fingerhut.Additionally I'd like to thank Dr. Stephen Griffin of the National Science Foundation, for helping us secure the foundational funding that made this symposium possible. Dr. Radha Nandkumar of the National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, must also be thanked for providing financial support to help us augment our student stipend program.Finally, several departments and individuals at our host institution, Indiana University Bloomington, deserve thanks. A great deal of the planning that went into this conference was done by Diane Jung, Charles Rondot, David Taylor, and Les Teach, of the Communications and Planning Office under the Office of the Vice President for Information Technology and CIO, and by Tawana Green and the staff of the IU Conference Bureau. Their assistance is much appreciated. In addition, the Indiana University School of Music generously supplied an extraordinary instrument, a fortepiano, for the mini-recital at the Mathers Museum. Sincerely ABSTRACTWe present a measure of the similarity of the long-term structure of musical pieces. The system deals with raw polyphonic data. Through unsupervised learning, we generate an abstract representation of music -the "texture score". This "texture score" can be matched to other similar scores using a generalized edit distance, in order to assess structural similarity. We notably apply this algorithm to the retrieval of different interpretations of the same song within a music database. MOTIVATIONMotivation for this system is our be...

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A sample of music information retrieval approaches

Downie

2004

J. Am. Soc. Inf. Sci.

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In this Perspectives edition of the Journal of the American Society for Information Science and Technology, we present articles specifically written as introductory overviews of ten important music information retrieval (MIR) research and development projects. As an MIR researcher myself, I am continuously awestruck by the multinational and multidisciplinary nature of MIR research. In this brief overview edition alone, we have authors from Australia, Canada, France, Great Britain, Italy, New Zealand, Poland, United States, Spain, and Taiwan. The disciplines represented both in this issue and in the broader MIR community include: general, music, and digital librarianship; computer science; audio engineering; signal processing; traditional information retrieval; musicology, music theory, and music psychology; the recording and music distribution business; human-computer interaction; and intellectual property law. Uniting this seemingly disparate aggregation is the common goal of providing the kind of robust access to the world's vast store of music-in all its varied forms (i.e., audio, symbolic, and metadata)-that we currently provide for textual materials.Yes, some MIR research teams do have visions of establishing the musical equivalent of Google.com. The enormous magnitude of the potential user base is too enticing not to be seduced by the monetary rewards associated with the successful launching of a Web-based MIR service. Beyond dreams of unlimited wealth, however, I believe most MIR research teams are motivated by two primary factors: (1) a basic love of music and (2) an overwhelming intellectual need to overcome the myriad difficulties posed by the inherent complexity of music. About the former factor, I have little to add but to say most, if not all, of the MIR researchers I have had the good fortune to meet have had long histories of amateur and professional music performance and/or scholarship. About the latter factor, I devote the following paragraph.Music information is a multifaceted amalgam that includes pitch, temporal (i.e., rhythm), harmonic, textual (i.e., lyrics, etc.), timbral (e.g., orchestration), editorial, and metadata elements. Music information is also extremely plastic. That is, any given work can have its specific pitches altered, its rhythms modified, its harmonies reset, its orchestration changed, its performances reinterpreted, and its performers arbitrarily chosen; yet, somehow, it remains the "same" piece of music as the "original." Because music information queries are founded in the same materials as music information, their creation and interpretation are also extremely plastic. Within this extraordinarily fluid environment, notions of "similarity" become particularly problematic. The problems associated with similarity in turn lead to difficulties in creating meaningful and useful outputs from MIR systems in response to the wide variety of potential music queries that will be submitted to them. Thus, the grand intellectual challenge facing past, present, and future MIR research ...

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Problems of music information retrieval in the real world

Cited by 90 publications

References 23 publications

Finding maximum-length repeating patterns in music databases

Finding maximum-length repeating patterns in music databases

Music information retrieval

A sample of music information retrieval approaches

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