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This thesis is presenting a method for generating short musical phrases usinga deep convolutional generative adversarial network (DCGAN). To train neuralnetwork were used datasets of classical and jazz music MIDI recordings. Ourapproach introduces translating the MIDI data into graphical images in a pianoroll format suitable for the network input size, using the RGB channels asadditional information carriers for improved performance. The network haslearned to generate images that are indistinguishable from the input data and,when translated back to MIDI and played back, include several musicallyinteresting rhythmic and harmonic structures. The results of the conductedexperiments are described and discussed, with conclusions for further work anda short comparison with selected existing solutions.

Wykorzystanie sztucznej inteligencji do generowania tre\'sci muzycznych