SampleMatch: Drum Sample Retrieval by Musical Context
This addresses a domain-specific problem for music producers by reducing workflow interruptions, though it is incremental as it applies contrastive learning to an existing task.
The paper tackled the problem of tedious drum sample selection in music production by developing an automatic retrieval system that ranks samples by fit to musical context, achieving alignment with human ratings in listening tests.
Modern digital music production typically involves combining numerous acoustic elements to compile a piece of music. Important types of such elements are drum samples, which determine the characteristics of the percussive components of the piece. Artists must use their aesthetic judgement to assess whether a given drum sample fits the current musical context. However, selecting drum samples from a potentially large library is tedious and may interrupt the creative flow. In this work, we explore the automatic drum sample retrieval based on aesthetic principles learned from data. As a result, artists can rank the samples in their library by fit to some musical context at different stages of the production process (i.e., by fit to incomplete song mixtures). To this end, we use contrastive learning to maximize the score of drum samples originating from the same song as the mixture. We conduct a listening test to determine whether the human ratings match the automatic scoring function. We also perform objective quantitative analyses to evaluate the efficacy of our approach.