Nowadays, generative adversarial networks (GANs) have proven themselves to be capable of creating hyper-realistic faces, animating paintings, colorizing sketches, and so on. However, these models can handle not only images but also text and audio. The context of this thesis is the research and exploration of Generative Adversarial Networks applied to music generation. The objective outcome is to create melodies that elicit a specific emotion in the listener. This is known as affective music composition. Emotions are an important aspect of music, and the ability to regulate this characteristic might find a variety of applications in generating soundtracks or melodies appropriate to different types of domains.