DanielleFrantz-NeuralNetworksForMusic - Neural Network...

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Neural Network Based Systems for Computer-Aided Musical Composition:Supervised x Unsupervised Learning ABSTRACT 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 I.2.6 [ Artificial Intelligent ]: Learning – Connectionism and Neural Nets. J.5.6 [ Arts and Humanities ]: Performing Arts . General Terms Algorithms, Experimentation, Human Factors. Keywords Artificial Neural Network, Self-Organized Maps, back- propagation, musical composition, learning. 1. INTRODUCTION 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 [5]. 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 [2] [3] [4] [5] [9] [10] [11]. 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. [1] [7] [8] [12] This process can be divided in two groups: supervised and unsupervised learning. [1] [7] [8] [12] 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. [7] [8] [12] In the unsupervised learning, the exact numerical output supposed
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This note was uploaded on 07/30/2011 for the course COP 4810 taught by Professor Staff during the Spring '11 term at University of Central Florida.

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DanielleFrantz-NeuralNetworksForMusic - Neural Network...

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