It is therefore clear that a domain-independent method that learns to map sequences to sequences would be useful. Traditional deep neural network techniques cannot be applied to generate music as they assume the inputs and targets/outputs to have fixed dimensionality and outputs to be independent of each other. Since music is a sequence of notes and chords, it doesn’t have a fixed dimensionality. This offers artists more creative freedom and ability to explore different domains of music. With the advent of deep learning, it has now become possible to generate music without the need for working with instruments artists may not have had access to or the skills to use previously. The current technological advancements have transformed the way we produce music, listen, and work with music. We will talk about how we can use deep learning to generate new musical beats. In this blog, we will extend the power of deep learning to the domain of music production. Deep Learning is on the rise, extending its application in every field, ranging from computer vision to natural language processing, healthcare, speech recognition, generating art, addition of sound to silent movies, machine translation, advertising, self-driving cars, etc.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |