1. Symbolic Musical Analysis CS 275B/Music 254

2. Practicalities CS 275B2016 Eleanor Selfridge-Field2

3. Course context  Music 253: Symbolic Musical Data  How to encode musical data sets  Notation  MIDI/sound  Logical content  Music 254Symbolic Music Analysis (Music Query, Analysis, and Style Simulation)  How to decode musical data sets  Query = data extraction  Analysis = data evaluation  Style simulation = data assessment and manipulation 2 2016 Eleanor Selfridge-Field CS 275B

4. Elements of the course 2016 Eleanor Selfridge-Field 4  Samples:  Query: Themefinder (melody)  Analysis: Humdrum (harmony, rhythm, melody)  Style simulation: Humdrum (involves integration of other operations) Project part: (a) literature/site survey (b) tool/software development (3) presentation and write-up CS 275B

5. Topic schedule 2016 Eleanor Selfridge-Field 5  Musical topics:  Week 2Melody; data sources; similarity  Week 3Harmony; current work  Week 4Rhythm; current work  Methodological topics (vary from year to year):  Week 5  Week 6  Week 7  Week 8 CS 275B

6. Contexts and resources CS 275B2016 Eleanor Selfridge-Field6

7. CS 275B2016 Eleanor Selfridge-Field7 Musical data Notation (Finale, SCORE) Logical data (kern) Musical data as IP Data interchange Sound (MIDI) Symbolic Musical Information Scheme No universal codeNo universal translator

8. 8 Data analysis Query/ retrieval Procedural analysis Musical data as IP Data interchange Musical style Symbolic Musical Analysis Scheme CS 275B 2016 Eleanor Selfridge-Field

9. Data resources at CCARH  Monophonic only  Essen database (folk music)  Themefinder repertories  Polyphonic  KernScores (keyboard)  MuseData (chamber, orchestral) CS 275B2016 Eleanor Selfridge-Field9

10. Query (Retrieval) via Themefinder CS 275B 2016 Eleanor Selfridge-Field 10 Search form Responses

11. 11 Music search 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 CS 275B 2016 Eleanor Selfridge-Field Intermediation Amico (1997-2005)

12. Major areas of music analysis CS 275B2016 Eleanor Selfridge-Field12

13. Feature extraction and analysis CS 275B2016 Eleanor Selfridge-Field13 Voices, textures Articulation, dynamics Tonal relations Off the grid From the work of Steven Malinowski

14. Musical style (analysis) CS 275B2016 Eleanor Selfridge-Field14 Score-based: conventional Score-based: Schenkerian (graphical) Performance-based: event-based, comparative analyses

15. DNA-sequencing algorithms CS 275B 2016 Eleanor Selfridge-Field 15 Dang Vu: feature propagation in improvised Vietnamese chamber music (adaptation of genetic analysis sw) Horse hemoglobin > music (Gene2Music, UCLA)

16. Musical style (simulation) CS 275B2016 Eleanor Selfridge-Field16 Work of David Cope (UCSC)

17. Data interchange  CS 275A  MusicXML  MEI (in progress)  MIDI (common default)  CS 275B (if relevant) CS 275B2016 Eleanor Selfridge-Field17 (Data supply)

18. Other possible areas of study  Procedural analysis  Structural analysis  Style/feature clusters  Methods from other disciplines  Linguistics  Mathematics  Psychology/cognition  Information theory  New representations  Non-Western music  Pre-tonal music  Post-tonal music  Audio/symbolic linking CS 275B2016 Eleanor Selfridge-Field18