Abstract : In this talk, I will present how from studying marketing I ended up doing a research on recurrent neural networks. I will introduce my project, the role of a music conductor and my dataset. I will present how I discovered coding in R and Python and why coding was just a way to achieve my goal. Finally, I will talk about how I answered my research question and the different models I tried.

1. How to marry tech
& classical music

2. My Background
Marketing Deep Learning
Statistician Master thesis
Application in
engineering
Master in BI
Today

3. The research project
Music Conducting
Department of the
University of Montreal
HEC Montreal
Polytechnique
Montreal
A short exercice
The first goals ?

4. The research project
HEC Montreal
Polytechnique
Montreal
The first goals ?
• Are the 2 arms really
desynchronized ?
• Where does the
variability of the
gestures come from ?
Music Conducting
Department of the
University of Montreal

5. Where the fun strats
THE RAW DATA
12 min
6 sensors
3 directions (X, Y and Z)
18 variables

6. Predict the type of gestures of
the right arm thanks to the positions
of the sensors

7. Labelling the dataset
12 min 267 bars
Time Bar Type of
gesture
0 1 A
1 1 A
2 1 A
3 1 A
4 2 A
5 2 A
6 3 B
7 3 B
8 3 B
+ =

8. Final dataset
FINAL DATASET
12 min
3 sensors
9 variables
Time Measure Type of gesture V1 … V9
0 1 A
1 1 A
2 1 A
3 1 A
4 2 A
5 2 A
6 3 B
7 3 B
8 3 B

9. Predict the type of gestures of
the right arm thanks to the positions
of the sensors

10. Recurrent Neural Networks

11. 1. Timeseries, sequences & multiples variables
Time 0 1 2 3 4 5 6 7 8
Measure 1 1 1 1 2 2 3 3 3
Type of
Gesture
A A A A A A B B B
V1
V2

12. 2. Random duration of sequences
Time 0 1 2 3 4 5 6 7 8
Measure 1 1 1 1 2 2 3 3 3
Type of
Gesture
A A A A A A B B B
V1
T=4 T=2 T=3
Interpolation
Padding

13. 3. Many timestamps for 1 output
Time 0 1 2 3 4 5 6 7 8
Measure 1 1 1 1 2 2 3 3 3
Type of
Gesture
A A A
A A
A B B
B
V1
Many to one architecture

14. 4. Classification
% of gestures
Type A Other types (19 types)
% of gestures
Type A Type B Type C Type D Type E Type F Type G Type H Other Types

15. Results & Limits
BEST MODEL
LSTM
Interpolation
90% of accuracy
(on test data)
MAIN LIMITS
GOALS
Dataset is too small
Only one conductor
& one piece
Academic project
Discover new statistical
methods
Explore these data

16. To conclude
4 Teachings
If you know the question, you will find a
solution
A technology is not an obstacle to a project
Models serve questions, questions don’t
serve models
Believe in you and your ideas … who knows
you might come and give a talk about it
someday !

17. Thank You