ml.lib: Robust, Cross-platform, Open-source Machine Learning for Max and Pure Data

Jamie Bullock Associate Professor of Music Technology

Birmingham City University

Ali Momeni Associate Professor of Art Carnegie Mellon University

Cont, A. (2008). Antescofo: Anticipatory Synchronization and Control of Interactive Parameters in Computer Music (pp. 33–40). Presented at the International Computer Music Conference, Belfast, Ireland: Ann Arbor, MI: Scholarly Publishing Office, University of Michigan Library.

Deyle, T., Palinko, S., & Poole, E. S. (2007). Hambone: A bio-acoustic gesture interface. … Computers. http://doi.org/10.1109/ISWC.2007.4373768

Sato, M., Poupyrev, I., & Harrison, C. (2012). Touché: enhancing touch interaction on humans, screens, liquids, and everyday objects. Chi, 483–492. http://doi.org/10.1145/2207676.2207743

Ono, M., Shizuki, B., & Tanaka, J. (2013). Touch & activate (pp. 31–40). Presented at the the 26th annual ACM symposium, New York, New York, USA: ACM Press. http://doi.org/10.1145/2501988.2501989

Poupyrev, Ivan, et al. "Botanicus Interacticus: interactive plants technology." ACM SIGGRAPH 2012 Emerging Technologies. ACM, 2012.

Nielsen,Usability Engineering (1993)

Syst

em a

ccep

tabi

lity

Social acceptability

Cost Compa-

tibility

Relia-bility

Etc.

Utility

Easy to learn

Efficient to use

Easy to remember

Few errors

Subjectively pleasing

Practical accepta-bility

Usefulness

Usability

. Raskin, The Humane Interface (2000)

• modeless: user actions should have the same effect regardless of the application’s state

addtrainmap

<class> <values …>3 .1 .4 .1 .5 .9 .2 .6

<values …>.1 .4 .1 .5 .9 .2 .6 3

recordManually segment time-varying input vectors

Dlibmlpack

Shark

• Efficient • Wide range of algorithms • Well supported • Good documentation

GRT

libsvm + others

GRT + FLEXT

=ml.lib

+ UCD

ClassificationAdaptive Boosting

Adaptive Naive BayesBootstrap Aggregator

Decision Trees Dynamic Time Warping

Finite State Machine Gaussian Mixture ModelHidden Markov Modelk-Nearest Neighbour

Linear Discriminant AnalysisMinimum DistanceParticle Classifier Random Forests

Support Vector Machines

Regression

Artificial Neural NetworkLinear Regression

Logistic RegressionMultidimensional Regression

Regression Tree

Feature Extraction

Peak DetectionMin / Max

Zero Crossings

Architecture

live

offline

algorithmtrainingvector

in-memorymodel

“add”

storedmodel

“train”

storeddata

“map”

“read / write”

in-memorydata

inputvector

outputvalue

Common Attributesprobs <0/1>

scaling <0/1>

Object-specific Attributesrandomize_training_order <0/1>

mode <0/1>

num_outputs <1..N>

Live Demo

Swept Frequency Sensing

Future Work

• Documentation!!! • Sort out HMMs • Implement GRT clustering algorithms • Possible threaded “train” • Maybe more feature extraction, LibXtract?

Thank You!