  The research in Deep Learning applications in sound and music computing have
gathered an interest in the recent years; however, there is still a missing
link between these new technologies and on how they can be incorporated into
real-world artistic practices. In this work, we explore a well-known Deep
Learning architecture called Variational Autoencoders (VAEs). These
architectures have been used in many areas for generating latent spaces where
data points are organized so that similar data points locate closer to each
other. Previously, VAEs have been used for generating latent timbre spaces or
latent spaces of symbolic music excepts. Applying VAE to audio features of
timbre requires a vocoder to transform the timbre generated by the network to
an audio signal, which is computationally expensive. In this work, we apply
VAEs to raw audio data directly while bypassing audio feature extraction. This
approach allows the practitioners to use any audio recording while giving
flexibility and control over the aesthetics through dataset curation. The lower
computation time in audio signal generation allows the raw audio approach to be
incorporated into real-time applications. In this work, we propose three
strategies to explore latent spaces of audio and timbre for sound design
applications. By doing so, our aim is to initiate a conversation on artistic
approaches and strategies to utilize latent audio spaces in sound and music
practices.
