4 research outputs found
CONLON: A pseudo-song generator based on a new pianoroll, Wasserstein autoencoders, and optimal interpolations
CONLON: A pseudo-song generator based on a new pianoroll, Wasserstein autoencoders, and optimal interpolations
We introduce CONLON, a pattern-based MIDI generation method that employs a new lossless pianoroll-like data description in which velocities and durations are stored in separate channels. CONLON uses Wasserstein autoencoders as the underlying generative model. Its generation strategy is similar to interpolation, where MIDI pseudo-songs are obtained by concatenating patterns decoded from smooth trajectories in the embedding space, but aims to produce a smooth result in the pattern space by computing optimal trajectories as the solution of a widest-path problem. A set of surveys enrolling 69 professional musicians shows that our system, when trained on datasets of carefully selected and coherent patterns, is able to produce pseudo-songs that are musically consistent and potentially useful for professional musicians. Additional materials can be found at https://paolo-f.github.io/CONLON/
On Verification and Validation in Engineering
Validation and verification are epistemic engineering activities that produce knowledge about quality and value of systems as they are being developed. In this chapter we contrast the two activities by investigating their respective roles in the engineering life cycle. After discussing some relevant classes of evaluative methods from engineering practice, we look at challenges and limitations in carrying out validation and verification, both fundamental and operational
Pattern-Based Music Generation with Wasserstein Autoencoders and PRC Descriptions
We demonstrate a pattern-based MIDI music generation system with a generation strategy based on Wasserstein autoencoders and a novel variant of pianoroll descriptions of patterns which employs separate channels for note velocities and note durations and can be fed into classic DCGAN-style convolutional architectures. We trained the system on two new datasets (in the acid-jazz and high-pop genres) composed by musicians in our team with music generation in mind. Our demonstration shows that moving smoothly in the latent space allows us to generate meaningful sequences of four-bars patterns