1. Using Musical Information: Query, Analysis, and Style Simulation Mus 254/CS 275B/SSP 253b Stanford University Spring Quarter

2. Musical data Notation (Finale, SCORE) Logical data (kern) Musical data as IP Data interchange Sound (MIDI) Music 253/CS 275A: Musical Data—Structure and Contents

3. 3 Data analysis Query/ retrieval Procedural analysis Musical data as IP Data interchange Musical style Mus 254/CS 275B: Musical Data—Applications

4. 4 Data = symbolic code  Why? Secure Scalable  What? Humdrum Toolkit **kern representation (CMN)

5. 5 1. Query: Approaches Query sound (commercial) Query meta-data (bibliographical) Query "meaning" (structural)

6. 6 Query: Data types Query by sound (commercial) Query by symbol (bibliographical) Query by "meaning" (analytical) Sound data Text data Semantic data

7. 7 Query: Symbolic approach Query sound (commercial) Query meta-data (bibliographical) Query "meaning" (structural) Query symbolic code

8. 8 Query: Sample applications Query by sound (Meldex) Query by symbol (Themefinder) Query by "meaning" (if ≠ metadata…) Sound data Symbolic data Semantic data

9. 9 Meta-data vs Semantic searching… fruit jars cloths peaches vase apricot blue green white teal (blue) forest (green) off-white CategoriesGradations Generic objects Specific objects Basic colorsSpecific colors Ambiguities

10. 10 2. Musical Style/3. Procedure Traditional studies Harmony Counterpoint Thematic form Melodic process Listening How? Observation, selection Observation, generation Observation (schematic) Observation, trial/error Real-time perception

11. 11 Style analysis: Extrapolation/Reduction

12. 12 Style analysis: Data types Analyze sound Analyze symbols Analyze semantics Sound data Symbolic data Semantic data

13. 13 Style analysis Corollaries: Test analyses ("generate and test") Explore human perception/recognition skills Application areas: Analysis facilitation (Andreas Kornstaedt) Style evaluation (Yi-Wen Liu) Style replication (David Cope) Non-Western music (Parag Chordia, Sachiko Deguchi, Craig Sapp)

14. 14 Style Simulation: Procedure Sound data Symbolic data Semantic data Create grammar Create MIDI data Parse and store EMI data Search EMI data Generate new work…

15. 15 EMI's First Brandenburg

16. 16 4. Data Acquisition/Interchange  Optical recognition  Preprocessing for query  Database development, management  Interchange standards (XML)  Applications: OMR: Walter Hewlett Preprocessing: Themefinder MusicXML: Michael Good

17. 17 5. Musical data as IP  Defining content  Methods of fixing data  Methods of identifying owner Content/fixed form (Eleanor) Watermarking (Yi-Wen)

18. 18 6. Data presentation Craig Sapp: Keyscapes Schubert: Piano Variations

19. 19 Possible Speakers 13 AprilFrauke Jorgenson (UC Davis) 19 April Petr Janata (neuroscience and music: CSLI) 27 April Craig Sapp (RHC, London) Late MayChristian Romming