Latent Space Exploration for Drum Samples

Sample-based electronic music production relies predominantly on sourcing, sampling, and transforming existing audio to create new compositions. With proliferation of digital music access, sample libraries, and online resource services, there is an increasing challenge in navigating and managing these extensive collections of musical material. This scenario underscores the necessity to explore new technological approaches to assist producers in efficiently handling and creatively using these resources. Building upon a previously developed generative adversarial network by the authors, this paper presents methods for latent space arithmetic, dimensionality reduction, and enhanced visualisations to simplify control interfaces for music producers. These techniques enable more intuitive music sample navigation and introduce new avenues for creative expression in neural audio synthesis. The efficacy of these methods is demonstrated through their application in generating diverse drum sounds, showcasing their practicality in music production.