Gesture Recognition & Machine Learning for Real-Time Musical Interaction

Rebecca FiebrinkAssistant Professor of Computer Science (also Music)Princeton University

Nicholas GillianPostdoc in Responsive EnvironmentsMIT Media Lab

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Introductions

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Outline

• ~40 min: Machine learning fundamentals

• ~1 hour: Wekinator: Intro & hands-on

• ~1 hour: Eyesweb: Intro & hands-on

• Wrap-up

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Models in gesture recognition & mapping

sensed action

interpretation

response (music, visuals, etc.)

computer

human + sensorssound, visuals,

etc.

model

•What is the current state (e.g., pose)?•Was a control motion performed?

• If so, which• How?

•What sound should result from this state, motion, motion quality, etc.?

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algorithm

trainingdata

Training

Supervised learning

model

inputs

outputs

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algorithm

trainingdata

Training

Supervised learning

model

inputs

outputsRunning

“Gesture 1” “Gesture 2” “Gesture 3”

“Gesture 1”

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Why use supervised learning?

• Models capture complex relationships from the data. (feasible)

• Models can generalize to new inputs. (accurate)

• Supervised learning circumvents the need to explicitly define mapping functions or models. (efficient)

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Data, features, algorithms, and models: the basics

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Features

• Each data point is represented as a feature vector

Example # Red(pixel1) Green(pixel1) Blue(pixel1) … Label

1 84 120 34 … Gesture1

2 43 25 85 … Gesture1

3 12 128 4 … Gesture2

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Features

• Good features can make a problem easier to learn!

Example # X(r_hand) Y(r_hand) Depth(r_hand Label

1 0.1 0.5 0.6 Gesture1

2 0.2 0.4 0.1 Gesture1

3 0.9 0.9 0.1 Gesture2

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Classification

This model: a separating line or hyperplane (decision boundary)

feature1

feat

ure2

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Regression

This model: a real-valued function of the input features

feature

ou

tpu

t

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Unsupervised learning

• Training set includes examples, but no labels

• Example: Infer clusters from data:

feature1

feat

ure2

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Temporal modeling

• Examples and inputs are sequential data points in time

• Model used for following, identification, recognition

Image: Bevilacqua et al., NIME 2007

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Temporal modeling

Image: Bevilacqua et al., NIME 2007

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How supervised learning algorithms work (the basics)

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The learning problem

• Goal: Build the best** model given the training data– Definition of “best” depends on context, assumptions…

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Which classifier is best?

Image from Andrew Ng

Competing goals:Accurately model training data**Accurately classify unseen data points**

“Overfit”“Underfit”

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A simple classifier: nearest neighbor

? feature1fe

atur

e2

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Another simple classifier: Decision tree

Images: http://ai.cs.umbc.edu/~oates/classes/2009/ML/homework1.html, http://nghiaho.com/?p=1300

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AdaBoost: Iteratively train a “weak” learnerImage from http://www.cc.gatech.edu/~kihwan23/imageCV/Final2005/FinalProject_KH.htm

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Support vector machine

• Re-map input space into a higher number of dimensions and find a separating hyperplane

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Choosing a classifier: Practical considerations

• k-Nearest Neighbor+ Can tune k to adjust smoothness of decision boundaries

- Sensitive to noisy, redundant, irrelevant features; prone to overfitting; weird in high dimensions

• Decision tree:+ Can prune to reduce overfitting, produces human-understandable model

- Can still overfit

• AdaBoost+ Theoretical benefits, less prone to overfitting

+ Can tune by changing base learner, number of training rounds

• Support Vector Machine+ Theoretical benefits similar to AdaBoost– Many parameters to tune, training can take a long time

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How to evaluate which classifier is better?

• Compute a quality metric– Metrics on training set (e.g, accuracy, RMS error)– Metrics on test set– Cross-validation

• Use it

Image from http://blog.weisu.org/2011/05/cross-validation.html

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Neural Networks

• TODO: Use nick’s slides

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Which learning method should you use?

• Classification (e.g., kNN, AdaBoost, SVM, decision tree):– Apply 1 of N labels to a static pose or state– Label a dynamic gesture, when segmentation & normalization are trivial

• E.g., feature vector is a fixed-length window in time

• Regression (e.g., with neural networks):– Produce a real-valued output (or vector of real-valued outputs) for each

feature vector

• Dynamic time warping, HMMs, other temporal models– Identify when a gesture has occurred, identify probable location within a

gesture, possibly also apply a label– Necessary when segmentation is non-trivial or online following is needed

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Suggested ML reading

• Bishop, 2006: Pattern Recognition & Machine Learning. Science and Business Media, Springer

• Duda, 2001: Pattern Classification, Wiley-Interscience

• Witten, 2005: Data Mining: Practical machine learning tools and techniques, Morgan Kaufmann

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Suggested NIME-y reading

• Lee, Freed, & Wessel, 1992. Neural networks for simultaneous classification and parameter estimation in musical instrument control. Adaptive and Learning Systems, 1706:244–55. (early example of ML in music)

• Hunt, A. and Wanderley, M. M. 2002. Mapping performer parameters to synthesis engines. Organised Sound 7, 2, 97–108. (learning as a tool for generative mapping creation)

• Chapter 2 of Rebecca’s dissertation: http://www.cs.princeton.edu/~fiebrink/thesis/ (historical/topic overview)

• Recent publications by F. Bevilacqua & team @ IRCAM (HMMs, gesture follower)

• TODO: Nick, anything else?

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Hands-on with Wekinator

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model(s)

.01, .59, .03, ....01, .59, .03, ....01, .59, .03, ....01, .59, .03, ...

5, .01, 22.7, …5, .01, 22.7, …5, .01, 22.7, …5, .01, 22.7, …

time

time

Feature extractor(s)

Parameterizable process

Inputs: from built-in feature extractors or OSC. Outputs: control ChucK patch or go elsewhere using OSC.

The Wekinator: Running in real time

OSC

OSC

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Brief intro to OSC

• Messages sent to host (e.g., localhost) and port (e.g., 6448)– Listener must listen on the same port

• Message contains message string (e.g., “/myOscMessage”) and optionally some data – Data can be int, float, string types– Listener code may listen for specific message strings &

data formats

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3.3098 Class24

Wekinator: Under the hood

Model1 Model2 ModelM

Feature1 Feature2 Feature3 FeatureN…

Parameter1 Parameter2 ParameterM

…

…

joystick_x joystick_y

pitchvolume

webcam_1

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3.3098 Class24

Under the hood

Model1 Model2 ModelM

Feature1 Feature2 Feature3 FeatureN…

Parameter1 Parameter2 ParameterM

…

…

Learning algorithms:Classification:

AdaBoost.M1J48 Decision TreeSupport vector machineK-nearest neighbor

Regression:Multilayer perceptron NNs

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Interactive ML with Wekinator

algorithm

trainingdata

Training

model

inputs

outputsRunning

“Gesture 1” “Gesture 2” “Gesture 3”

“Gesture 1”

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Interactive ML with Wekinator

algorithm

trainingdata

Training

model

inputs

outputsRunning “Gesture 1”

“Gesture 1” “Gesture 2”

creating training data

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Interactive ML with Wekinator

algorithm

trainingdata

Training

inputs

outputsRunning

“Gesture 1” “Gesture 2”

model

“Gesture 1”

creating training data…evaluating the trained model

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Interactive ML with Wekinator

algorithm

trainingdata

Training

model

inputs

outputsRunning “Gesture 1”

“Gesture 1” “Gesture 2” “Gesture 3”

creating training dataevaluating the trained model…

modifying training data (and repeating)

interactive machine learning

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Time to play

• Discrete classifier

• Continuous neural net mapping

• Free-for-all