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Nime 2015 slides 02 ali 2
This document introduces ml.lib, an open-source machine learning library for Max and Pure Data that provides robust and cross-platform machine learning algorithms. Ml.lib integrates efficient libraries like Dlib and mlpack to implement classification, regression, and feature extraction algorithms. It aims to make machine learning accessible for interactive media applications by exposing common attributes and allowing training and classification of data in Max and PD. Examples of possible uses include phone orientation sensing, gesture mapping, and audio analysis. Future work may include improved documentation, implementing more algorithms from GRT, and multi-threaded training.
Nime 2015 slides 02 ali 2
- 1. 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
- 2. 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.
- 3. Deyle, T., Palinko,S., & Poole, E. S. (2007). Hambone: A bio-acoustic gesture interface. … Computers. http://doi.org/10.1109/ISWC.2007.4373768
- 4. 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
- 5. 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
- 6. Poupyrev, Ivan, etal. "Botanicus Interacticus: interactive plants technology." ACM SIGGRAPH 2012 Emerging Technologies. ACM, 2012.
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- 8. . Raskin, TheHumane Interface (2000) : user actions should have the same effect regardless of the appl
- 9. add train map <class> <values …> 3.1 .4 .1 .5 .9 .2 .6 <values …> .1 .4 .1 .5 .9 .2 .6 3
- 10. record Manually segment time-varyinginput vectors
- 11. Dlib mlpack Shark • Efficient • Widerange of algorithms • Well supported • Good documentation GRT libsvm + others
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- 13. Classification Adaptive Boosting Adaptive NaiveBayes Bootstrap Aggregator Decision Trees Dynamic Time Warping Finite State Machine Gaussian Mixture Model Hidden Markov Model k-Nearest Neighbour Linear Discriminant Analysis Minimum Distance Particle Classifier Random Forests Support Vector Machines Regression Artificial Neural Network Linear Regression Logistic Regression Multidimensional Regression Regression Tree Feature Extraction Peak Detection Min / Max Zero Crossings
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- 15. Common Attributes probs <0/1> scaling<0/1> Object-specific Attributes randomize_training_order <0/1> mode <0/1> num_outputs <1..N>
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- 20. Future Work • Documentation!!! •Sort out HMMs • Implement GRT clustering algorithms • Possible threaded “train” • Maybe more feature extraction, LibXtract?
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