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Conditional GAN for Diatonic Harmonic Sequences Generation in a VR Context
Abstract
The use of AI models for music generation receives an important attention from scientific communities. Different architectures of deep learning neural networks have been applied for this specific task, such as Recurrent Neural Networks (RNN), Generative Adversarial Networks (GAN), Autoencoders, Variational Autoencoders (VAE) and Transformers. One of the important aspects of the generation process is the possibility to control the output by providing the input parameters, and a conditional generation was widely used in a computer vision domain to meet this need. In a present research we adopt the principles of conditional generation using GAN architecture and convolutions, applying them to a temporal domain, resulting in building a conditional GAN for diatonic harmonic sequences generation. The model is further used as a core feature of the VR module for computer-aided composition from “Graphs in harmony learning” VR project, where the sequence generation is conditioned by the user's input and the result is mapped to 3D representations of the generated chords.
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