Sound, like images or text, is a complex, high-dimensional kind of data. Music in particular has many complexities: it could involve on or serveral musicians, has a temporal structure, and can be divided into thematically related segments, All of these components are incorporated into models such as MuseGAN, as mentioned earlier, which uses GANs to generate these various components and synthesize them into realistic, yet synthetic, musical tracks. I will describe the implementation of MuseGAN and its variants in Chapter 11, Composing Music with Generative Models.
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