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Our in house ML models can estimate pitch and extract chords from audio streams, on the fly, in realtime. Our proprietary models can estimate the 3d position and orientation of real instruments from a single photograph. The algorithms are trained using a mix of real and synthetic data, and can work with reflective surfaces and repeating patterns. We've developed new machine learning algorithms that can synthesize novel and kinematically accurate 3d musical performance from just a midi audio file, for the use in education and AR / VR. Our tools can perform advanced full body and hand inverse kinematics to fit the same 3d musical performance to different avatars.


Biles

AAAI Conferences

The author has been performing with GenJam, the Genetic Jammer, for nearly 20 years and has accumulated a wealth of experiences in performing live jazz with technology. This paper presents a discussion of the use of technology in jazz, both from the performer's and from the audience's perspective, and it proposes a classification scheme for live performance that is geared to mainstream performing situations.


How To Implement Machine Learning Algorithm Performance Metrics From Scratch With Python

@machinelearnbot

This article was written by Jason Brownlee. Jason is the editor-in-chief at MachineLearningMastery.com.He has a Masters and PhD in Artificial Intelligence, has published books on Machine Learning and has written operational code that is running in production.


How To Implement Machine Learning Algorithm Performance Metrics From Scratch With Python

#artificialintelligence

After you make predictions, you need to know if they are any good. There are standard measures that we can use to summarize how good a set of predictions actually are. Knowing how good a set of predictions is, allows you to make estimates about how good a given machine learning model of your problem, In this tutorial, you will discover how to implement four standard prediction evaluation metrics from scratch in Python. You must estimate the quality of a set of predictions when training a machine learning model. Performance metrics like classification accuracy and root mean squared error can give you a clear objective idea of how good a set of predictions is, and in turn how good the model is that generated them.


How To Implement Machine Learning Algorithm Performance Metrics From Scratch With Python

@machinelearnbot

This article was written by Jason Brownlee. Jason is the editor-in-chief at MachineLearningMastery.com.He has a Masters and PhD in Artificial Intelligence, has published books on Machine Learning and has written operational code that is running in production. After you make predictions, you need to know if they are any good. There are standard measures that we can use to summarize how good a set of predictions actually are. Knowing how good a set of predictions is, allows you to make estimates about how good a given machine learning model of your problem, In this tutorial, you will discover how to implement four standard prediction evaluation metrics from scratch in Python.