LoopNet: Musical Loop Synthesis Conditioned On Intuitive Musical Parameters
This work addresses the need for composers to create custom musical loops based on intuitive controls, though it appears incremental as it builds on existing architectures like Wave-U-Net and MIR models.
The authors tackled the problem of generating musical loops by developing LoopNet, a feed-forward generative model that synthesizes loops conditioned on intuitive musical parameters like rhythm, harmony, and timbre, resulting in audio loops evaluated for quality and usability for composers.
Loops, seamlessly repeatable musical segments, are a cornerstone of modern music production. Contemporary artists often mix and match various sampled or pre-recorded loops based on musical criteria such as rhythm, harmony and timbral texture to create compositions. Taking such criteria into account, we present LoopNet, a feed-forward generative model for creating loops conditioned on intuitive parameters. We leverage Music Information Retrieval (MIR) models as well as a large collection of public loop samples in our study and use the Wave-U-Net architecture to map control parameters to audio. We also evaluate the quality of the generated audio and propose intuitive controls for composers to map the ideas in their minds to an audio loop.