Machine learning-based computational models have been successfully applied to a broad range of complex information processing tasks, including those that involve retrieving specific data items from large archives. Researchers at the Sony Computer Science Laboratories (CSL) in France have been trying to develop machine learning techniques that could help music producers to easily identify and retrieve specific audio samples from a database. To this end, Stefan Lattner, a researcher at Sony CSL, recently introduced SampleMatch, a machine learning-based model that can automatically retrieve drum samples that match a specific music track from large archives. His model is set to be presented in December at the ISMIR 2022 conference, a leading event that focuses on music information retrieval. "Our music team at Sony CSL is working on AI that could make the life of music producers easier," Stefan Lattner, one of the researchers who carried out the study, told TechXplore.

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.