Lartillot, Olivier

A version of PatMinr has been submitted to the MIREX task on Discovery of Repeated Themes &amp; Sections. PatMinr can find repetitions of sequential patterns from monophonic sequences represented in a symbolic format. This document presents the model in more details, and specifies the particular parameters used in the version submitted to the MIREX competition

    Aalborg UniversitetPatMinrLartillot, OlivierPublication date:2014Document VersionEarly version, also known as pre-printLink to publication from Aalborg UniversityCitation for published version (APA):Lartillot, O. (2014). PatMinr: In-depth motivic analysis of symbolic monophonic sequences. Paper presented atMusic Information Retrieval Evaluation eXchange, Taipei, Taiwan, Province of China.General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            ? Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            ? You may not further distribute the material or use it for any profit-making activity or commercial gain            ? You may freely distribute the URL identifying the publication in the public portal ?Take down policyIf you believe that this document breaches copyright please contact us at vbn@aub.aau.dk providing details, and we will remove access tothe work immediately and investigate your claim.Downloaded from vbn.aau.dk on: May 01, 2017PATMINR: IN-DEPTH MOTIVIC ANALYSIS OF SYMBOLICMONOPHONIC SEQUENCESOlivier LartillotAalborg University, Department of Architecture, Design and Media Technology, Denmarkolartillot@gmail.comABSTRACTA version of PatMinr [2] has been submitted to the MIREXtask on Discovery of Repeated Themes & Sections [1].PatMinr can find repetitions of sequential patterns frommonophonic sequences represented in a symbolic format.This document presents the model in more details, andspecifies the particular parameters used in the version sub-mitted to the MIREX competition.1. VERSION SUBMITTED TO MIREXThe MiningSuite 1 is a new open-source framework foraudio and music analysis implemented in Matlab decom-posed into modules. In particular, the MusMinr moduleconcentrates on music analysis and features an option formotivic analysis. The actual pattern analysis computationis taken care by the PatMinr module dedicated to patternanalysis. Version 0.7.1 of the MiningSuite has been sub-mitted to MIREX.1.1 mus.minr: pattern discoveryMusic sequences in symbolic format can be loaded usingthe text file format defined in the MIREX task, using thefollowing syntax:mus.minr(’filename.txt’, ’Motif’)where mus.minr loads the text file and perform a selec-tion of music analysis methods on each successive note.Here, only the ’Motif’ method is called, correspondingto the motivic analysis, which uses the pattern mining al-gorithms in the PatMinr module of the MiningSuite.In this version, the algorithm searches for all possiblerepetitions of subsequences along any subset of musicaldimensions. The subsequences can be heterogeneous, inthe sense that the musical dimensions describing each suc-cessive note and interval of a subsequence can vary along1 Freely available at http://code.google.com/p/miningsuite/.c© Olivier Lartillot.Licensed under a Creative Commons Attribution 4.0 International Li-cense (CC BY 4.0). Attribution: Olivier Lartillot. “PATMINR: IN-DEPTH MOTIVIC ANALYSIS OF SYMBOLIC MONOPHONIC SE-QUENCES”, 15th International Society for Music Information RetrievalConference, 2014.the subsequence. Redundant structural information is fil-tered out without loss of information through closed andcyclic pattern mining [2].In order to reduce the computation time, in the MIREXversion, only two musical dimensions are considered:• chromatic dimension: MIDI pitch of each note andinter-pitch interval between successive notes• diatonic dimension: staff height of each note andcorresponding interval between successive notesIn particular, gross contour is not taken into account, andno rhythmical analysis is performed.1.2 mus.output: post-filtering and result formattingA post-processing routine selecting the most important pat-terns and formatting the results into a text file is coded inthe M file +mus/output.m.Only patterns longer than 11 nodes 2 are selected.The paradigmatic sheaf bundling described in section3.2 in [2] is implemented in the function sheaf, also partof the M file +mus/output.m.2. ALGORITHMIC DETAILS2.1 Incremental one-pass approachThe approach is incremental, progressively analysing themusical sequence through one single pass. The main loopof this incremental approach (Algorithm 1) consists in achronological scanning of the musical sequence: the anal-ysis is carried out for each successive note of the sequencesuccessively. This loop is coded in the process_notesfunction in the M file related to the mus.minr operator(/+mus/minr.m).Algorithm 1 One-pass scanning of musical sequencefor each successive note ni in the sequence doProcessNote(ni)end for2 Each pattern is a branch in the pattern tree. The first node of eachbranch corresponds to the root of the tree, which is not associated to anynote description. Hence patterns longer than 11 nodes correspond to se-quence longer than 10 notes.2.2 Occurrences extensionAs explained in sections 2.2 and 3.3 in [2], integrating anew note consists in checking:• whether pattern occurrence(s) ending at the previousnote can be extended with the new note,• whether the new note initiates the start of a new pat-tern occurrence.These test are detailed in Algorithm 2. The two tests de-scribed above correspond respectively to the second loop(lines 6-14) and to the last operation (lines 15-16).Each note is considered with respect to the previousnote (since pitch intervals are integrated in the note de-scription). For that reason, in the actual code, each note in-stantiates a syntagmatic connection between that note andits previous note, which is an object of the pat.syntagmclass. The algorithm 2 is included in the constructor methodof pat.syntagm.Algorithm 2 ProcessNote(ni)for each cycle C ending at ni−1 doP is the pattern related toC, PM is the periodic modeland φ is the current phase on that period.ExtendCycle(C, ni,P ,PM ,φ)end forfor each pattern occurrence O ending at ni−1, in de-creasing order of specificity doP is the pattern of Ofor each pattern Pj equal to or more general than P ,in decreasing order of specificity, except pattern equalto or more general than more general occurrences doExtendOccurrence(O, ni, Pj)end forend forExtendOccurrence([], ni,∅), where ∅ represents the pat-tern tree root, i.e. the empty pattern.For each occurrence, the list of pattern extension oper-ations is detailed in Algorithm 3. This algorithm is codedin the method memorize of the pattern occurrence classpat.occurrence.2.3 Pattern CyclicityAs explained in section 4 in [2], the successive repetitionsof a periodic pattern leads to the formation of a single chainof states, where each state C is related to:• a particular note ni in the piece of music• the periodic model PM , which is the pattern repre-senting the whole preceding period• the pattern P that is constructed for that particularnote. In the simple case, it is a prefix of the periodicmodel PM at phase φ, i.e., P = PM (φ)• the phase φ on that period: a phase φ = 0 meansthat the periodic pattern is now completely recon-structed, so that a new occurrence of that pattern willAlgorithm 3 ExtendOccurrence(O,ni,P )d is note ni’s descriptionfor each possible extension E of P doif E’s description entirely conforms to d thenExtend O with note n following extension E.else if E’s description partially conforms to d thend′ is the description common to d and Eif there is no extension of P with description d′thenCreate a new extension E′ of P based on de-scription d′.Extend O with note n following extension E′.end ifend ifend forLook at description d in the continuation memory re-lated to pattern P .if there exists a similar older context (occurrenceO′ fol-lowed by note n′) and P extended with d is closed thenCreate a new extension E of pattern P based on de-scription d.Extend O′ with note n′ following extension E, andstore the following note’s description in the continua-tion memory related to E.Extend O with note n following extension E.elseAdd the new note n in the continuation memory.end ifstart to be constructed from that point, a phase φ > 0indicates that the current note is associated with theφth note of the periodic patternAlgorithm 4 shows how to detect the successive phasesand how to generalise progressively the period if needed.If at a given phase φ, the next note does not extend thepattern P = PM (φ) following the description given byPM (φ+1), but only partially, then for the next phase φ+1,the new pattern P is more general than PM (φ + 1). Oncethe whole period is generated, the resulting more generalperiod P becomes the new periodic pattern PM used asguide for the next period. This algorithm is coded in themethod track_cycle in pat.occurrence class.3. REFERENCES[1] T. Collins. MIREX 2013: Discovery of RepeatedThemes and Sections, 2013. http://www.music-ir.org/mirex/wiki/2013:Discovery of Repeated Themes& Sections Accessed on 14 August 2014.[2] O. Lartillot: “In-Depth Motivic Analysis Based onMultiparametric Closed Pattern and Cyclic SequenceMining,” Proceedings of the International Symposiumon Music Information Retrieval, 2014.Algorithm 4 ExtendCycle(C, ni, P ,PM ,φ)d is note ni’s descriptionfor each possible extension E of P doN ← []if bothE’s and PM (φ+1)’s descriptions entirely con-form to d thenN ← E.else if bothE’s and PM (φ+1)’s descriptions partiallyconform to d thend′ is the description common to d and Eif there is no extension of P with description d′thenCreate a new extension E′ of P based on d′.end ifN ← E′end ifif N 6= [] thenif φ =length(PM ) thenφ′ ← 0, PM ← Pelseφ′ ← φ+ 1end ifExtend C: C ′ with note ni, pattern N , phase φ′,pattern model PMend ifend for

PatMinr:In-depth motivic analysis of symbolic monophonic sequences

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PatMinr:In-depth motivic analysis of symbolic monophonic sequences

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