Computer generated music can serve as an important tool for composers to enhance their musical creativity and expand their knowledge about musical composition. This makes computer music generation a well researched field that has expanded greatly with the development of deep neural networks. Currently, generating longer stylistically consistent music sequences is a big challenge. Another challenge we often encounter is controlling the properties of the generated music. We tackled both of the mentioned challenges by implementing a denoising diffusion probabilistic model capable of generating original music notation that follows the form of the soundtracks for the NES game console, while the generation process can be guided by automatic emotion annotations. We managed to implement a diffusion model, which we evaluated with the help of a musical Turing test, where in 43% cases computer generated melodies confused the test subjects for human written melodies.
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