SampleMatch: Drum Sample Retrieval by Musical Context

TL;DR

SampleMatch is a system that automatically retrieves drum samples fitting a musical context, using contrastive learning to align sample selection with aesthetic judgment, thereby streamlining digital music production.

Contribution

This work introduces a contrastive learning approach for automatic drum sample retrieval based on musical context, enhancing creative workflow in music production.

Findings

Human ratings align with automatic scoring in listening tests.

Objective analyses show improved sample relevance to musical context.

Abstract

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.