Neural Network Based Systems for Computer-Aided
Musical Composition:Supervised x Unsupervised Learning
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.
Categories and Subject Descriptors
]: Learning –
Arts and Humanities
Algorithms, Experimentation, Human Factors.
propagation, musical composition, learning.
The musical computation, including reproduction and generation,
has attracted researches a long time ago. Interests in musical
composition by computers, specifically, date back to the 1950’s
when Markov chains were used to generate melodies . Since
music students often learn composition by examples, early
approaches were motivated and based on pattern analysis in
existing music. More recently, the artificial neural networks
(ANNs) have been deployed as models for learning musical
processes       .
The ANNs, also known as connectionist systems, represent a non-
algorithmic computation form inspired on the human brain
structure and processing. In this new computing approach,
computation is performed by a set of several simple processing
units, the neurons, connected in a network and acting in parallel.
The neurons are connected by weights, which store the network
knowledge. To represent a desired solution of a problem, the
ANNs perform a training or learning stage, which consists of
showing a set of examples (dataset training) to the network so that
it can extract the necessary features to represent the given
information.     This process can be divided in two
groups: supervised and unsupervised learning.    
The supervised learning is performed by input-output mapping.
i.e., the input patterns and correspondent desired outputs are given
to the network by a supervisor. The goal of this learning approach
is to adjust the network parameters until the network can answer
correctly to any input pattern from training set. After the training
stage, the network can generalize what was learned. It occurs
when the network produces suitable outputs for inputs that were
not include in training.
  
In the unsupervised learning, the exact numerical output supposed