Network Modulation Synthesis: New Algorithms for Generating Musical Audio Using Autoencoder Networks
This work addresses the challenge for musicians and audio engineers in creating varied audio outputs using autoencoder-based synthesis, though it appears incremental as it builds on existing autoencoder methods.
The paper tackles the problem of generating musical audio by introducing network modulation synthesis, a framework that uses autoencoder networks and novel algorithms to simplify navigating the latent space for audio creation, resulting in easier generation of complex auditory possibilities compared to handcrafted encodings.
A new framework is presented for generating musical audio using autoencoder neural networks. With the presented framework, called network modulation synthesis, users can create synthesis architectures and use novel generative algorithms to more easily move through the complex latent parameter space of an autoencoder model to create audio. Implementations of the new algorithms are provided for the open-source CANNe synthesizer network, and can be applied to other autoencoder networks for audio synthesis. Spectrograms and time-series encoding analysis demonstrate that the new algorithms provide simple mechanisms for users to generate time-varying parameter combinations, and therefore auditory possibilities, that are difficult to create by generating audio from handcrafted encodings.