  In this paper AlimNet (With respect to great musician, Alim Qasimov) an
auxiliary generative adversarial deep neural network (ACGAN) for generating
music categorically, is used. This proposed network is a conditional ACGAN to
condition the generation process on music tracks which has a hybrid
architecture, composing of different kind of layers of neural networks. The
employed music dataset is MICM which contains 1137 music samples (506 violins
and 631 straw) with seven types of classical music Dastgah labels. To extract
both temporal and spectral features, Short-Time Fourier Transform (STFT) is
applied to convert input audio signals from time domain to time-frequency
domain. GANs are composed of a generator for generating new samples and a
discriminator to help generator making better samples. Samples in
time-frequency domain are used to train discriminator in fourteen classes
(seven Dastgahs and two instruments). The outputs of the conditional ACGAN are
also artificial music samples in those mentioned scales in time-frequency
domain. Then the output of the generator is transformed by Inverse STFT
(ISTFT). Finally, randomly ten generated music samples (five violin and five
straw samples) are given to ten musicians to rate how exact the samples are and
the overall result was 76.5%.
