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Generating Convincing Harmony Parts with Simple Long Short-Term Memory Networks 
Faitas, Andrei; Baumann, Synne Engdahl; Næss, Torgrim Rudland; Tørresen, Jim; Martin, Charles Patrick (Chapter / Bokkapittel / PublishedVersion; Peer reviewed, 2019)
Generating convincing music via deep neural networks is a challenging problem that shows promise for many applications including interactive musical creation. One part of this challenge is the problem of generating convincing ...
Facial Expression Recognition Using Robust Local Directional Strength Pattern Features and Recurrent Neural Network 
Rokkones, Anders Skibeli; Uddin, Md Zia; Tørresen, Jim (Book chapter / Bokkapittel / AcceptedVersion; Peer reviewed, 2019)
This work proposes a novel facial expression recognition approach to contribute to better human-machine interactions. To do that, edge features in facial expression images are combined with a recurrent neural network (RNN) ...
RaveForce: A Deep Reinforcement Learning Environment for Music 
Lan, Qichao; Tørresen, Jim; Jensenius, Alexander Refsum (Chapter / Bokkapittel / PublishedVersion; Peer reviewed, 2019)
RaveForce is a programming framework designed for a computational music generation method that involves audio sample level evaluation in symbolic music representation generation. It comprises a Python module and a SuperCollider ...
Parameterized Melody Generation with Autoencoders and Temporally-Consistent Noise 
Weber, Aline; Alegre, Lucas N.; Tørresen, Jim; Castro da Silva, Bruno (Chapter / Bokkapittel / PublishedVersion; Peer reviewed, 2019)
We introduce a machine learning technique to autonomously generate novel melodies that are variations of an arbitrary base melody. These are produced by a neural network that ensures that (with high probability) the melodic ...
An Interactive Musical Prediction System with Mixture Density Recurrent Neural Networks 
Martin, Charles Patrick; Tørresen, Jim (Chapter / Bokkapittel / PublishedVersion; Peer reviewed, 2019)
This paper is about creating digital musical instruments where a predictive neural network model is integrated into the interactive system. Rather than predicting symbolic music (e.g., MIDI notes), we suggest that predicting ...
Comparison of Probabilistic Models and Neural Networks on Prediction of Home Sensor Events 
Casagrande, Flavia Dias; Tørresen, Jim; Zouganeli, Evi (Chapter / Bokkapittel / AcceptedVersion; Peer reviewed, 2019)
We present results and comparative analysis on the prediction of sensor events in a smart home environment with a limited number of binary sensors. We apply two probabilistic methods, namely Sequence Prediction via Enhanced ...
 
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Date Issued
2019 (6)
Document Type
Bokkapittel (6)
Author
Tørresen, Jim (6)
Martin, Charles Patrick (2)Alegre, Lucas N. (1)Baumann, Synne Engdahl (1)Casagrande, Flavia Dias (1)... View MorePeer ReviewedPeer reviewed (6)
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