Learning Musicianship for Automatic Accompaniment

Transcription

1 Learning Musicianship for Automatic Accompaniment Gus (Guangyu) Xia Roger Dannenberg School of Computer Science Carnegie Mellon University

2 2 Introduction: Musical background Interaction Expression Rehearsal

3 3 Introduction: Technical background Musicianship Expressive Performance Interaction Score following and automatic accompaniment Expressive Interactive Performance

4 Introduction: Problem definition For interactive music performance, how can we build artificial performers that automatically improve their ability to sense and coordinate with human musicians expression with rehearsal experience? How to interpret the music based on the expression of human musicians? How to distill models from rehearsals? What are the limits of validity of the learned models? How many rehearsals are needed? We start from piano duets, focusing on expressive timing and expressive dynamics. 4

5 5 Outline Introduction Data Collection Methods Demos Conclusion & Future Work

6 6 Current Data Collection Musicians: 10 music master students play duet pieces in 5 pairs. Music pieces: 3 pieces of music are selected, Danny boy, Serenade (by Schubert), and Ashokan Farewell. Each pair performs every piece of music 7 times. Recording settings: Recorded by electronic pianos with MIDI output.

7 7 Outline Introduction Data Collection Methods Demos Conclusion & Future Work

8 Method Overview From local to general Local: low-dimensional feature space, only apply to certain notes General: high-dimensional feature space, apply to the whole piece of music Base line: Score Following Automatic Accompaniment Real time Real time predicted next note s performance time next note s reference time Reference time Reference time 8

9 Method (1): Note-specific approach Idea: Expressive timings of the notes are linearly correlated. Predict the expressive timing of 2 nd piano by the expressive timing of 1 st piano. = [,,, ] =,,, Model: = + "#$( ) 9

10 10 Result: Note-specific approach Mean Absolute Error: BL: Note-8: Note-34: Time residual (sec) BL Note 8 Note Score time (sec)

11 Method (2): Rhythm-specific approach Idea: Notes with same score rhythm context share parameters. Introduces an extra dummy variable to encode the score rhythm context of each note. = [,,, ] =,,, Model: "( =,) "( +,) "#$( ) 11

12 12 Result: Rhythm-specific approach Mean Absolute Error: BL: Rhythm-4: Rhythm-8: Time residual (sec) BL Rhythm 4 Rhythm Score time (sec)

13 13 Method (3): General feature approach Idea: Make the model more general. Predict the expressive timing by considering more than score rhythm context. =,,, =,,, Model: =

14 14 Regularization: Group Lasso Idea: Reduces the burden for training. Discover the dominant features that could predict the expressive timings. Solve: min " +

15 15 Result: General feature approach Mean Absolute Error: (ONLY 4 training pieces) BL: LR: Glasso: Time residual (sec) BL LR Glasso Score time (sec)

16 16 Method (4): LDS approach Idea: Add another regularization by adjacent notes. Lower dimensional hidden mental states that control the expressive timings. u t- 1 u t u t+1 z t- 1 z t z t+1 y t- 1 y t y t+1 Model: = + + ~(0, ) = + + ~(0, )

17 17 Result: LDS (horizontal regularization) Mean Absolute Error: (ONLY 4 training pieces) BL: LR: LDS: Time residual (sec) BL LR LDS Score time (sec)

18 18 A Global View Time residual(sec) Serenade Danny boy Ashokan Farewell 0 BL Note 4 Rhythm 4 Glasso 4 Note 8 Rhythm 8 Glasso 8 Note 34 Rhythm 34Glasso 34 Methods and training size

19 19 Outline Introduction Data Collection Methods Demos Conclusion & Future Work

20 20 Some Initial Audio Demo Base Line: Note-specific approach: 34 training examples General feature approach, group lasso: 4 training examples

21 21 Future Work Cross-piece models Performer-specific models Online learning and decoding Plugin with music robots

22 22 Conclusion An artificial performer for interactive performance Learn musicianship from rehearsal experience A combination of expressive performance and automatic accompaniment Much better prediction just based on 4 rehearsals

23 23 Q&A

24 Spectral Learning(1): Oblique projections u t- 1 u t u t+1 z t- 1 z t z t+1 y t- 1 y t y t+1 ( ) = [ ] We don t know the future. Partially explain future observations based on the history [ 0] 0 24

25 25 Spectral Learning(2): state estimation u t- 1 u t u t+1 z t- 1 z t z t+1 = " y t- 1 y t y t+1 States estimation by SVD = Σ = (Σ )(Σ ) Moreover, enforce a bottleneck by throwing out near-zero singular values and corresponding columns in U and V.

26 26 Spectral Learning(3): Estimate parameter u t- 1 u t u t+1 z t- 1 z t z t+1 y t- 1 y t y t+1 = + + ~(0, ) = + + ~(0, ) Based on estimated hidden states, the parameters could be estimated from the following equation: = +