2020/08/10 by Guillaume Alain, Alain, Guillaume, Maxime Chevalier-Boisvert +7 · 1 citation
Computer Science · Mathematics · Neuroscience · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Music Technology and Sound Studies #Music and Audio Processing #Neuroscience and Music Perception #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2008.04391
openalex publication_date 2020/08/10 · openalex created_date 2020/08/18 · arxiv created 2020/08/26 · arxiv updated 2020/08/28 · openalex updated_date 2026/07/28
DeepDrummer is a drum loop generation tool that uses active learning to learn the preferences (or current artistic intentions) of a human user from a small number of interactions. The principal goal of this tool is to enable an efficient exploration of new musical ideas. We train a deep neural network classifier on audio data and show how it can be used as the core component of a system that generates drum loops based on few prior beliefs as to how these loops should be structured. We aim to build a system that can converge to meaningful results even with a limited number of interactions with the user. This property enables our method to be used from a cold start situation (no pre-existing dataset), or starting from a collection of audio samples provided by the user. In a proof of concept study with 25 participants, we empirically demonstrate that DeepDrummer is able to converge towards the preference of our subjects after a small number of interactions.