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WRITER IDENTIFICATION IN HISTORICAL MUSIC SCORES

This project focuses on preserving historical music scores and their associated knowledge through the creation of a digital archive. It aims to provide various retrieval possibilities for musicologists and librarians and utilizes handwriting analysis tools for manual and automatic analysis of the scores.

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WRITER IDENTIFICATION IN HISTORICAL MUSIC SCORES

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  1. WRITER IDENTIFICATION IN HISTORICAL MUSIC SCORES 2004

  2. PROJECT PARTNERS Musicologists Information Sources Image Processing Data Extraction Database Group Storage Data Management Retrieval Data Access

  3. PROBLEMS IN MUSICOLOGY How to preserve historical material, and associate and manage corresponding knowledge? There exist multiple collections of historical manuscripts all over the world. One of them is the 18th century music scores collection at the University of Rostock. Who was the writer of a music score? ... What relation did he have to the composer? What is the paper made of? Who was the owner, user and collector of the copy? „Each music score writer has a typical and unique handwriting signature, which can be defined on a set of a „few“ handwriting characteristics.

  4. SOLUTIONS • Creating a digital archive for storing handwritten historical music • scores and their corresponding metadata • Providing various retrieval possibilities for the historical material • Providing interactive electronic access to music scores and their • metadata for musicologists, librarians and other library users • Integrating specialized knowledge structures and image • processing tools for manual and automatic analysis of • handwriting in music scores

  5. DIGITAL MUSIC SCORES Library Catalogue Scanned Pages Bibliographical Data Work and Source Description Handwriting Analysis Tools Digital Images Handwriting Characteristics

  6. HANDWRITING CHARACTERISTICS • Clef • Slant • Note Stems • Note Flags • Note Beams • Accidentals • Rastrum • Note Head Form • Time Signatures • Bar Lines • Writing Habits • Note Beams Offset • Rests

  7. KNOWLEDGE REPRESENTATIONS • Similarity Matrix • Feature Base

  8. WRITER IDENTIFICATION Music Score Feature Base Handwriting Classification Feature -Extraction Result from the Classification Handwriting Clusters Feature Vectors Handwriting Clustering empirical optimization

  9. MANUAL HANDWRITING ANALYSIS - EXAMPLE ...1.2.2 closed loop ...1.2.2.1 ...1.2.2.3 acsending or descending ...1.2.2.1.7 ...1.2.2.1.3 ...1.2.2.1.5 left or right of G-point or loop clusters of scribes similarity matrix ... 1.2.2.1.7 ... 1.2.2.1 0.6 ... 1.2.2.3 0.2 ... 1.2.2.1.3 0.8 ... 1.2.2.1.5 0.4

  10. AUTOMATIC HANDWRITING ANALYSIS Query Image Smoothing, histogram equalization, morphological operations Consistency Check Image Preprocessing Score Images Writer Identification (unknown writer) Consistency Check Segmentation Image Analysis Feature Extraction and indexing for known writers Recognize Music Symbols of interest Object Recognition Feature Vectors Pattern Matching, Classification Writer Identification Query Result

  11. TOOLS AND TECHNIQUES • Data Storage • object relational databases (IBM DB2) • Data Retrieval • Full-text Search • Attribute Search • Knowledge-based Retrieval (manual handwriting analysis) • Visual features-based Retrieval (automatic handwriting analysis) • Access – servlet-based framework of abstract structures for accessing data in specialized digital archives • Metadata Browsing • Image Browsing • Metadata Search • Writer Identification (handwriting analysis)

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