Add to ORKGIn this paper, we introduce new methods and discuss results of text-based LSTM (Long Short-Term Memory) networks for automatic music composition. The proposed network is designed to learn relationships within text documents that represent chord progressions and drum tracks in two case studies. In the experiments, word-RNNs (Recurrent Neural Networks) show good results for both cases, while character-based RNNs (char-RNNs) only succeed to learn chord progressions. The proposed system can be used for fully automatic composition or as semi-automatic systems that help humans to compose music by controlling a diversity parameter of the model
We demonstrate two generative models created by training a recurrent neural network (RNN) with three...
In this paper, to automatically generate a music for the melody part by deep learning with training ...
Advances in Recurrent Neural Network (RNN) techniques have caused an explosion of problems posed tha...
In this paper, we introduce new methods and discuss results of text-based LSTM (Long Short-Term Memo...
We apply deep learning methods, specifically long short-term memory (LSTM) networks, to music transc...
We apply deep learning methods, specifically long short-term memory (LSTM) networks, to music transc...
The automatic composition of music with long-term structure is a central problem in music generation...
Neural networks, and especially long short-term memory networks (LSTM), have become increasingly pop...
Humans are able to learn and compose complex, yet beautiful, pieces of music as seen in e.g. the hig...
Recurrent (neural) networks have been deployed as models for learning musical processes, by computat...
Long Short-Term Memory (LSTM) neural networks have been ef- fectively applied on learning and genera...
The goal of this project is to train a machine to compose Baroque Fugue/Canon by using Long-Short Te...
In this paper we take a connectionist machine learning approach to the problem of metre perception a...
Master's Project (M.S.) University of Alaska Fairbanks, 2019In this paper, we compare the effectiven...
Drum Track Generation is the problem of composing the rhythmic component of music. Drum Track Genera...
We demonstrate two generative models created by training a recurrent neural network (RNN) with three...
In this paper, to automatically generate a music for the melody part by deep learning with training ...
Advances in Recurrent Neural Network (RNN) techniques have caused an explosion of problems posed tha...
In this paper, we introduce new methods and discuss results of text-based LSTM (Long Short-Term Memo...
We apply deep learning methods, specifically long short-term memory (LSTM) networks, to music transc...
We apply deep learning methods, specifically long short-term memory (LSTM) networks, to music transc...
The automatic composition of music with long-term structure is a central problem in music generation...
Neural networks, and especially long short-term memory networks (LSTM), have become increasingly pop...
Humans are able to learn and compose complex, yet beautiful, pieces of music as seen in e.g. the hig...
Recurrent (neural) networks have been deployed as models for learning musical processes, by computat...
Long Short-Term Memory (LSTM) neural networks have been ef- fectively applied on learning and genera...
The goal of this project is to train a machine to compose Baroque Fugue/Canon by using Long-Short Te...
In this paper we take a connectionist machine learning approach to the problem of metre perception a...
Master's Project (M.S.) University of Alaska Fairbanks, 2019In this paper, we compare the effectiven...
Drum Track Generation is the problem of composing the rhythmic component of music. Drum Track Genera...
We demonstrate two generative models created by training a recurrent neural network (RNN) with three...
In this paper, to automatically generate a music for the melody part by deep learning with training ...
Advances in Recurrent Neural Network (RNN) techniques have caused an explosion of problems posed tha...