Executing Hierarchical Interactive Scores in ReactiveML

Transcript

1. Executing Hierarchical Interactive Scores in ReactiveML Jaime Arias Joint work with Myriam Desainte-Catherine, Sylvain Salvati and Camilo Rueda Laboratoire Bordelais de Recherche en Informatique (LaBRI) Université de Bordeaux Journées d’Informatique Musicale Bourges, May 2014 1 
   
2. Motivation • Interactive scores [10] is a formalism for writing and executing interactive scenarios. • A wide range of application such as video games and museum installations. ◦ Non-linear behaviour ⇝ conditionals, loops . • I-score1. The execution model is based on Hierarchical Time Stream Petri Nets [19]. ◦ Very static. ◦ New features would require a complete redesign of the network. • There are some problems: ◦ Hierarchical aspects. ◦ Feedback during execution. ◦ Complexity of the network. 1http://i-score.org Jaime Arias (LaBRI) Executing Hierarchical Interactive Scores in ReactiveML 1/18 1/18
3. Motivation • ReactiveML is a programming language for implementing interactive systems [16]. • Synchronous reactive model of Boussinot [8]. • It provides a discrete model of time, clear semantics, synchronous, deterministic and dynamic features. • It has been previously used in music applications [4] showing to be ◦ Very expressive. ◦ Efficient. ◦ Capable of interacting with the environment during the performance of complex scores. ◦ Well suited for building prototypes easily. Jaime Arias (LaBRI) Executing Hierarchical Interactive Scores in ReactiveML 2/18 2/18
4. This talk is about … We explore a new way to define and implement interactive scores, aiming at a more dynamic and simple model. Jaime Arias (LaBRI) Executing Hierarchical Interactive Scores in ReactiveML 3/18 3/18
5. Outline What is essential to know? Interactive Scores ReactiveML INScore A synchronous model for IS Improving the visualization Demo Summary Future work Jaime Arias (LaBRI) Executing Hierarchical Interactive Scores in ReactiveML 4/18 4/18
6. What is essential to know? Interactive Scores 2 4 3 5 1 6 0 r2 r1 r3 r4 r5 r6 r7 Figure: Example of an interactive score. skip Jaime Arias (LaBRI) Executing Hierarchical Interactive Scores in ReactiveML 5/18 5/18
7. What is essential to know? Interactive Scores Figure: Example of an interactive score designed in i-score. Jaime Arias (LaBRI) Executing Hierarchical Interactive Scores in ReactiveML 6/18 6/18
8. What is essential to know? ReactiveML 1 let process emit_tic period tic = 2 let start = Unix.gettimeofday () in 3 let next = ref (start +. period) in 4 loop 5 let current = Unix.gettimeofday() in 6 if (current >= !next) then begin 7 emit tic (); 8 next := !next +. period 9 end; 10 pause 11 end Figure: Example of ReactiveML language.2 2Based on the work of Baudart et al. [5] Jaime Arias (LaBRI) Executing Hierarchical Interactive Scores in ReactiveML 7/18 7/18
9. What is essential to know? ReactiveML logical time seconds signal tic; #run emit_tic 1.0 tic Jaime Arias (LaBRI) Executing Hierarchical Interactive Scores in ReactiveML 8/18 8/18
10. What is essential to know? ReactiveML logical time seconds signal tic; #run emit_tic 1.0 tic start = Unix.gettimeofday () next = ref (start +. period) current = Unix.gettimeofday() current >= next ? Jaime Arias (LaBRI) Executing Hierarchical Interactive Scores in ReactiveML 8/18 8/18
11. What is essential to know? ReactiveML logical time seconds signal tic; #run emit_tic 1.0 tic start = Unix.gettimeofday () next = ref (start +. period) current = Unix.gettimeofday() current >= next ? no Jaime Arias (LaBRI) Executing Hierarchical Interactive Scores in ReactiveML 8/18 8/18
12. What is essential to know? ReactiveML logical time seconds signal tic; #run emit_tic 1.0 tic start = Unix.gettimeofday () next = ref (start +. period) current = Unix.gettimeofday() current >= next ? no pause Jaime Arias (LaBRI) Executing Hierarchical Interactive Scores in ReactiveML 8/18 8/18
13. What is essential to know? ReactiveML logical time seconds signal tic; #run emit_tic 1.0 tic start = Unix.gettimeofday () next = ref (start +. period) current = Unix.gettimeofday() current >= next ? Jaime Arias (LaBRI) Executing Hierarchical Interactive Scores in ReactiveML 8/18 8/18
14. What is essential to know? ReactiveML logical time seconds signal tic; #run emit_tic 1.0 tic start = Unix.gettimeofday () next = ref (start +. period) current = Unix.gettimeofday() current >= next ? no Jaime Arias (LaBRI) Executing Hierarchical Interactive Scores in ReactiveML 8/18 8/18
15. What is essential to know? ReactiveML logical time seconds signal tic; #run emit_tic 1.0 tic start = Unix.gettimeofday () next = ref (start +. period) current = Unix.gettimeofday() current >= next ? no pause Jaime Arias (LaBRI) Executing Hierarchical Interactive Scores in ReactiveML 8/18 8/18
16. What is essential to know? ReactiveML logical time seconds signal tic; #run emit_tic 1.0 tic start = Unix.gettimeofday () next = ref (start +. period) current = Unix.gettimeofday() current >= next ? Jaime Arias (LaBRI) Executing Hierarchical Interactive Scores in ReactiveML 8/18 8/18
17. What is essential to know? ReactiveML logical time seconds signal tic; #run emit_tic 1.0 tic start = Unix.gettimeofday () next = ref (start +. period) current = Unix.gettimeofday() current >= next ? yes Jaime Arias (LaBRI) Executing Hierarchical Interactive Scores in ReactiveML 8/18 8/18
18. What is essential to know? ReactiveML logical time seconds signal tic; #run emit_tic 1.0 tic 1 start = Unix.gettimeofday () next = ref (start +. period) current = Unix.gettimeofday() current >= next ? yes emit tic () Jaime Arias (LaBRI) Executing Hierarchical Interactive Scores in ReactiveML 8/18 8/18
19. What is essential to know? ReactiveML logical time seconds signal tic; #run emit_tic 1.0 tic 1 start = Unix.gettimeofday () next = ref (start +. period) current = Unix.gettimeofday() current >= next ? yes emit tic () pause Jaime Arias (LaBRI) Executing Hierarchical Interactive Scores in ReactiveML 8/18 8/18
20. What is essential to know? ReactiveML logical time seconds signal tic; #run emit_tic 1.0 tic 1 2 Jaime Arias (LaBRI) Executing Hierarchical Interactive Scores in ReactiveML 8/18 8/18
21. What is essential to know? INScore Figure: Example of a score in INScore. Jaime Arias (LaBRI) Executing Hierarchical Interactive Scores in ReactiveML 9/18 9/18
22. A synchronous model for IS Temporal relations time fixed interval interactive interval duration minimum duration maximum duration interaction point enabled Figure: Representation of intervals. Jaime Arias (LaBRI) Executing Hierarchical Interactive Scores in ReactiveML 10/18 10/18
23. A synchronous model for IS Boxes 1 2 r1 r2 (a) Visual 1 signal pb1_s, hb1_s, hb2_s, r1_s, r2_s; 2 let ip = ("/stop",[Int32 3]); 3 let start_m = ("/box/1",[String "start "]); 4 let stop_m = ("/box/1",[String "stop"]); 5 let r1 = Fixed (Finite 2, r1_s); 6 let r2 = Fixed (Finite 0, r2_s); 7 let p_box = Process (1, 8 Fixed (Finite 2, pb1_s), 9 start_m, stop_m); 10 let h_box = Hierarchical (2, 11 [p_box], 12 [(2,1,r1);(1,2,r2)], 13 Interactive ((Finite 2, hb1_s), 14 (Infinite, hb2_s), ip)) (b) Text Figure: Representation of boxes. Jaime Arias (LaBRI) Executing Hierarchical Interactive Scores in ReactiveML 11/18 11/18
24. A synchronous model for IS Merging intervals 2 3 1 r1 r2 Figure: Example of a box with two preceding temporal relations. Jaime Arias (LaBRI) Executing Hierarchical Interactive Scores in ReactiveML 12/18 12/18
25. A synchronous model for IS Merging intervals time fixed interval 1 fixed interval 2 merge fixed (1,2) (a) Merging fixed intervals. interactive interval 1 interactive interval 2 merge interactive (1,2) min max min max min max interaction point enabled time (b) Merging interactive intervals. Figure: Merging intervals. Jaime Arias (LaBRI) Executing Hierarchical Interactive Scores in ReactiveML 13/18 13/18
26. Improving the visualization Interpreter Sync. Observer INScore signals Pure Data OSC OSC OSC Figure: Diagram of the visualization system. Jaime Arias (LaBRI) Executing Hierarchical Interactive Scores in ReactiveML 14/18 14/18
27. Demo Let’s try it! see scenario Jaime Arias (LaBRI) Executing Hierarchical Interactive Scores in ReactiveML 15/18 15/18
28. Summary • We presented a synchronous interpreter of multimedia interactive scores. • We showed that the model is simple. • Contrary to the Petri Net model, our approach allows to model precisely the hierarchical behaviour of boxes. • We could prototype new features easily and execute living code using the toplevel of ReactiveML. • We explored the use of INScore to develop a graphical interface that provides a real-time visualization of the execution of the score. • Our approach would allow to execute dynamic processes unlike Petri Nets. Jaime Arias (LaBRI) Executing Hierarchical Interactive Scores in ReactiveML 16/18 16/18
29. Future work • Formalize the operational semantics (generic model). • Take advantage of ReactiveML properties for prototyping new features. ◦ Add conditionals and loops for describing non-linear behaviour. • Analyze data-flow programming languages like Lucid Synchrone for handling streams. • Carry out a performance testing. • Implement a compiler i-score ⇝ Rml-score • Verification of properties. • Improve the visual environment in INScore. Jaime Arias (LaBRI) Executing Hierarchical Interactive Scores in ReactiveML 17/18 17/18
30. Thank you for your attention. Jaime Arias (LaBRI) Executing Hierarchical Interactive Scores in ReactiveML 18/18 18/18
31. Executing Hierarchical Interactive Scores in ReactiveML Jaime Arias Joint work with Myriam Desainte-Catherine, Sylvain Salvati and Camilo Rueda Laboratoire Bordelais de Recherche en Informatique (LaBRI) Université de Bordeaux Journées d’Informatique Musicale Bourges, May 2014 1 
    
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