Learning a Latent Space of Multitrack Measures

Some of these models learn a latent space: a lowerdimensional representation that can be mapped to and from the object space. A major advantage of such latent space models is that many operations that would be difficult to perform in the object space, like morphing between two objects in a semantically meaningful way, become straightforward arithmetic in the latent space. It has even been claimed that latent space models can augment human understanding of the object domain [6]. Latent space models have already been trained for several musical concepts including raw waveforms of notes [12], melodies and drum tracks [33], and playlists [38]. Such models are also frequently used for music recommendations [23], where both user "taste" and song "style" are reasoned about in terms of latent vectors. In this paper, we present a latent space model of individual measures of music with multi-instrument polyphony and dynamics. One way to think about such objects is as musical textures; however, we do not model the audio itself but rather use a symbolic representation of the music. This latent space model allows us to perform a number of intuitive operations: - Sample a measure from the prior distribution to generate novel music from scratch.