Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
Neural networks: a systematic introduction
Neural networks: a systematic introduction
A First Look at Music Composition using LSTM Recurrent Neural Networks
A First Look at Music Composition using LSTM Recurrent Neural Networks
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Recurrent Neural Networks for Music Computation
INFORMS Journal on Computing
Algorithmic compositions based on discovered musical patterns
Multimedia Tools and Applications
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This ongoing project describes neural network applications for helping musical composition using as inspiration the natural landscape contours. We propose supervised and unsupervised learning approaches, by using Back-Propagation-Through-Time (BPTT) and Self Organizing Maps (SOM) neural networks. In the supervised learning, the network learns certain aspects of musical structure by means of measure examples taken from melodies of the training set and uses these measures learned to compose new melodies using as input the extracted data of the landscapes contour. In the unsupervised learning, the network also uses measure examples as input during training and the extracted data of the landscapes contour in the composition stage. The obtained results show the viability of both approaches.