DESIGNING A SIMPLE NEURAL COMPOSER Nikos Kofidis 1 , Efthymios Kotsialos 2 , Athanasios Margaris 3 and Manos Roumeliotis 4 Department of Applied Informatics, University of Macedonia Egnatias 156 Str, GR 540 06 Thessaloniki, Hellas 1 email: kofid@uom.gr , 2 email: ekots@uom.gr , 3 email: amarg@uom.gr , 4 email: manos@uom.gr Abstract: This paper describes the design of a neural network, which has the ability to produce a sequence of chords obeying the main rules of the harmony of classical music. Specifically, when the network’s input layer is fed with a chord, its output layer produces an acceptable chord of the same scale. The input signal consists of four digits. Three of them are used for the binary representation of the chords. The fourth one takes values in the interval [0,1] and plays the role of a direction pointer. Its value determines the next chord in the sequence. The network’s output consists of six neurons and produces a decoded binary output. The network is trained by the Back Propagation algorithm. During the recall phase, the network is at first stimulated by the tonic chord of the scale and continues working due to the feedback of the output chord to the input layer. The direction pointer neuron of the input layer is fed by random numbers, thus contributing to the enrichment of the resulting harmony. Keywords: Neural Networks, music, composition, back-propagation algorithm. 1. INTRODUCTION Both pattern recognition of musical structures and composition techniques have been approached by researchers who have successfully used a variety of neural structures as analysis or composition tools. Bharucha J.J. and Todd M.P [1] make use of neural networks (supervised and competitive learning) to simulate the musical structure of tonal pieces. Scarborough D. L., Miller O. B., Jones A. J [10], attempt tonal analysis of musical pieces designing proper neural structures. Sano H., Jenkins B. K.[9], constructed a neural network that models pitch Perception while Laden B., Keefe H. D [7] have worked with neural networks on the topic of pitch perception and chord classification. Gjerdingen Robert O [2] has experimented with ‘ART’ networks’ ability, based on Grossberg’s[3,4,5] adaptive resonance theory, to categorize complex musical patterns of pieces written in classic period. Todd Peter [11] set up a sequential neural network to produce melodic structures while Kohonen Teuvo, Laine Pauli, Tirts Kalev, and Torkkola Kari [6] constructed networks that store a number of composition rules in order to produce a useful tool for musical composition. Lewis J.P [8] used supervised and reinforcement learning techniques to produce a sequence of notes and chords as an output of carefully designed neural structure. The main task of the current work is the design of a neural network that can simulate simple composition procedures established by the composers of classical music. Specifically, the network should be able to produce a sequence of chords strictly limited by some of the basic rules of the classical harmony. The following table presents the most often-used acceptable chord sequences.