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The SIMSSA Project: Search as access to digital music libraries

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Presentation on theme: "The SIMSSA Project: Search as access to digital music libraries"— Presentation transcript:

1 The SIMSSA Project: Search as access to digital music libraries
Emily Hopkins SIMSSA Project Manager, McGill University Workshop on SIMSSA XV, IAML, Leipzig, 28 July 2018

2 SSHRC Partnership Grant (2014-2021)
PI: Ichiro Fujinaga (McGill University) Partners include the British Library, Bodleian Libraries at Oxford, Bibliothèque Nationale de France, Bavarian State Library, New York Philharmonic Archives, Alexander Street Press, RILM, and RISM Switzerland among others PARTNER LIST -- Include all partners who will be at IAML!

3 How it works: Library digitizes scores Optical Music Recognition
Symbolic Encoding with MEI Search and Analysis

4 How do we access the scores?
How can we teach computers to read musical scores? How will music search and analysis work?

5 How do we access the scores?

6 International Image Interoperability Framework

7 MusicLibs.net Music libs
Front page randomly serves up previews of scores in our database

8 How can we teach computers to read musical scores?

9 Optical Character Recognition
Makes images of text machine-readable XML

10 Optical Music Recognition
Makes images of sheet music machine-readable MIDI, MusicXML, MEI

11 Music Encoding Initiative (MEI)
<music>     <body>         <mdiv>             <score>                 <scoreDef meter.count="2" meter.unit="4" key.sig="3s">                     <staffGrp symbol="line">                         <staffDef n="1" label="Singstimme." lines="5" clef.shape="G" clef.line="2"/>                         <staffGrp symbol="brace" label="Pianoforte.">                             <staffDef n="2" lines="5" clef.shape="G" clef.line="2"/>                             <staffDef n="3" lines="5" clef.shape="F" clef.line="4"/>                         </staffGrp>                     </staffGrp>                 </scoreDef>             </score>         </mdiv>     </body> </music> Example borrowed from the MEI tutorial at music-encoding.org; music is Robert Schumann’s Der Abendstern.

12 Commercial OMR

13 Salzinnes Antiphonal CDN Hsmu M2149.L4 http://cantus.simssa.ca
Whatever ground truth we give it!

14 Pixel.js: Making ground truth data

15 Pixelwise Classification
Calvo-Zaragoza, Jorge. “Pixelwise classification for music document analysis.” Presented at the Workshop on SIMSSA XII, August 7, 2017.

16 Interactive Classifier: Identifying glyphs & training our OMR

17 Neon.js: Correcting OMR output

18 Scoring-up Tool Slide courtesy of Thomae Elias, Martha Eladia, Julie Cumming, and Ichiro Fujinaga. “Automatic Scoring up of Music in Mensural Notation.” Presented at the 46th Medieval and Renaissance Music Conference, Maynooth, Ireland, July 2018.

19 Encoding rules for context-dependent mensural notation
Relate to Measuring Polyphony Slide courtesy of Thomae Elias, Martha Eladia, Julie Cumming, and Ichiro Fujinaga. “Automatic Scoring up of Music in Mensural Notation.” Presented at the 46th Medieval and Renaissance Music Conference, Maynooth, Ireland, July 2018.

20

21 Crowdsourced OMR Correction
Making tools more user-friendly Collaboration with Partner organizations and user communities

22 How will music search and analysis work?

23 Melodic Search Garfinkle, David & Peter Schubert. “Computer-Assisted Corpus Analysis Finds a Signature Progression in Willaert and Palestrina.” Presented at the 46th Medieval and Renaissance Music Conference, Maynooth, Ireland, July 2018.

24 https://patternfinder.elvisproject.ca/

25 Corpus Studies Schubert, Peter, and Julie Cumming. “Another Lesson from Lassus: Using Computers to Analyse Counterpoint.” Early Music 43, no. 4 (November 2015): 577–86.

26 Desmond, Karen, Emily Hopkins, and Sam Howes
Desmond, Karen, Emily Hopkins, and Sam Howes. “Measuring Polyphony: Analysing Stylistic Change in the French Motet Repertory, C ” Presented at the Workshop on SIMSSA VIII, McGill University, Montreal, QC, May 21, 2016. Comparing downbeats (only) to offbeats (only)

27 Percentage of perfect sonorities for all pieces

28

29 Arthur, Claire, Julie Cumming, and Peter Schubert
Arthur, Claire, Julie Cumming, and Peter Schubert. “Computer-Assisted Modal Identification.” Presented at the 46th Medieval and Renaissance Music Conference, Maynooth, Ireland, July 2018.

30 Machine learning and composer identification
McKay, Cory, Tristano Tenaglia, Julie Cumming, and Ichiro Fujinaga. “Using Statistical Feature Extraction to Distinguish the Styles of Different Composers.” Presented at the Medieval and Renaissance Music Conference, Prague, Czech Republic, July 4, 2017.

31 Thank you! https://simssa.ca/ emily.hopkins@mcgill.ca @simssaproject
@e_a_hopkins

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