Unrolled Creative Adversarial Network For Generating Novel Musical Pieces

• URL: http://arxiv.org/abs/2501.00452v1
• Date: Tue, 31 Dec 2024 14:07:59 GMT
• Title: Unrolled Creative Adversarial Network For Generating Novel Musical Pieces
• Authors: Pratik Nag,
• Abstract summary: generative adversarial networks (GANs) and their counterparts have been explored by very few researchersfor music generation. In this paper, a classical system was employed alongside a new system to generate creative music. GANs are capable of generating novel outputs given a set of inputs to learn from and mimic their distribution.
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• Abstract: Music generation has been established as a prominent topic in artificial intelligence and machine learning over recent years. In most recent works on RNN-based neural network methods have been applied for sequence generation. In contrast, generative adversarial networks (GANs) and their counterparts have been explored by very few researchersfor music generation. In this paper, a classical system was employed alongside a new system to generate creative music. Both systems were designed based on adversarial networks to generate music by learning from examples. The classical system was trained to learn a set of music pieces without differentiating between classes, whereas the new system was trained to learn the different composers and their styles to generate a creative music piece by deviating from the learned composers' styles. The base structure utilized was generative adversarial networks (GANs), which are capable of generating novel outputs given a set of inputs to learn from and mimic their distribution. It has been shown in previous work that GANs are limited in their original design with respect to creative outputs. Building on the Creative Adversarial Networks (CAN) , this work applied them in the music domain rather than the visual art domain. Additionally, unrolled CAN was introduced to prevent mode collapse. Experiments were conducted on both GAN and CAN for generating music, and their capabilities were measured in terms of deviation from the input set.

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