LACTOSE: Linear Array of Conditions, TOpologies with Separated Error-backpropagation -- The Differentiable "IF" Conditional for Differentiable Digital Signal Processing

There has been difficulty utilising conditional statements as part of the neural network graph (e.g. if input $> x$, pass input to network $N$). This is due to the inability to backpropagate through branching conditions. The Linear Array of Conditions, TOpologies with Separated Error-backpropagation (LACTOSE) Algorithm addresses this issue and allows the conditional use of available machine learning layers for supervised learning models. In this paper, the LACTOSE algorithm is applied to a simple use of DDSP, however, the main point is the development of the "if" conditional for DDSP use. The LACTOSE algorithm stores trained parameters for each user-specified numerical range and loads the parameters dynamically during prediction.

Music Representing Corpus Virtual: An Open Sourced Library for Explorative Music Generation, Sound Design, and Instrument Creation with Artificial Intelligence and Machine Learning

Music Representing Corpus Virtual (MRCV) is an open source software suite designed to explore the capabilities of Artificial Intelligence (AI) and Machine Learning (ML) in Music Generation, Sound Design, and Virtual Instrument Creation (MGSDIC). The software is accessible to users of varying levels of experience, with an emphasis on providing an explorative approach to MGSDIC. The main aim of MRCV is to facilitate creativity, allowing users to customize input datasets for training the neural networks, and offering a range of options for each neural network (thoroughly documented in the Github Wiki). The software suite is designed to be accessible to musicians, audio professionals, sound designers, and composers, regardless of their prior experience in AI or ML. The documentation is prepared in such a way as to abstract technical details, thereby making it easy to understand. The software is open source, meaning users can contribute to its development, and the community can collectively benefit from the insights and experience of other users.