SampleMatch is a machine learning-based model that helps music producers sort drum sample libraries by relevance to a particular track. Based on contrastive learning, SampleMatch sorts a drum sample library based on what would match best with the input track, providing a matching score.

SampleMatch can be a valuable tool for music producers, as it simplifies the tedious process of drum sample selection, which can be time-consuming. By using SampleMatch, producers can input their music track at any stage of production and automatically sort their drum sample library to retrieve samples that would match best with it. This helps to increase efficiency and productivity, allowing producers to focus more on the creative aspects of music production. Moreover, the model’s ability to learn aesthetic principles and general rules that musicians should follow when mixing their music could be insightful and helpful for future music production efforts.

This research is led by Stefan Lattner, researcher at Sony CSL - Paris.

Related Publication

ISMIR 2022
SampleMatch: Drum Sample Retrieval by Musical Context
Stefan Lattner
contrastive learning, drum samples selection, musical context

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