# SampleMatch: Drum Sample Retrieval by Musical Context

arXiv: https://arxiv.org/abs/2208.01141 · HTML: https://mlsanity.com/paper/2208.01141/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 30 references that are in the map.
- First posted on arXiv 1 Aug 2022.

## Builds on (most cited first, 24 of 24)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning](https://mlsanity.com/paper/2105.04906/) (2021)
- [DDSP: Differentiable Digital Signal Processing](https://mlsanity.com/paper/2001.04643/) (2020)
- [Contrastive Learning of General-Purpose Audio Representations](https://mlsanity.com/paper/2010.10915/) (2020)
- [Decoupled Contrastive Learning](https://mlsanity.com/paper/2110.06848/) (2021)
- [BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation](https://mlsanity.com/paper/2103.06695/) (2021)
- [Contrastive Learning of Musical Representations](https://mlsanity.com/paper/2103.09410/) (2021)
- [Multi-Format Contrastive Learning of Audio Representations](https://mlsanity.com/paper/2103.06508/) (2021)
- [Speech SIMCLR: Combining Contrastive and Reconstruction Objective for Self-supervised Speech Representation Learning](https://mlsanity.com/paper/2010.13991/) (2020)
- [Multimodal Self-Supervised Learning of General Audio Representations](https://mlsanity.com/paper/2104.12807/) (2021)
- [ESResNet: Environmental Sound Classification Based on Visual Domain Models](https://mlsanity.com/paper/2004.07301/) (2020)
- [The Piano Inpainting Application](https://mlsanity.com/paper/2107.05944/) (2021)
- [Conformer-Based Self-Supervised Learning for Non-Speech Audio Tasks](https://mlsanity.com/paper/2110.07313/) (2021)
- [High-Level Control of Drum Track Generation Using Learned Patterns of Rhythmic Interaction](https://mlsanity.com/paper/1908.00948/) (2019)
- [Neural Loop Combiner: Neural Network Models for Assessing the Compatibility of Loops](https://mlsanity.com/paper/2008.02011/) (2020)
- [Modeling the Compatibility of Stem Tracks to Generate Music Mashups](https://mlsanity.com/paper/2103.14208/) (2021)
- [DrumGAN VST: A Plugin for Drum Sound Analysis/Synthesis With Autoencoding Generative Adversarial Networks](https://mlsanity.com/paper/2206.14723/) (2022)
- [An Exploratory Study on Perceptual Spaces of the Singing Voice](https://mlsanity.com/paper/2111.08196/) (2021)
- [Similarity measures for vocal-based drum sample retrieval using deep convolutional auto-encoders](https://mlsanity.com/paper/1802.05178/) (2018)
- [Deep Conditional Representation Learning for Drum Sample Retrieval by Vocalisation](https://mlsanity.com/paper/2204.04651/) (2022)

## Built on by (most cited first, 8 of 8)

- [COCOLA: Coherence-Oriented Contrastive Learning of Musical Audio Representations](https://mlsanity.com/paper/2404.16969/) (2024)
- [Stem-JEPA: A Joint-Embedding Predictive Architecture for Musical Stem Compatibility Estimation](https://mlsanity.com/paper/2408.02514/) (2024)
- [Singer Identity Representation Learning using Self-Supervised Techniques](https://mlsanity.com/paper/2401.05064/) (2024)
- [Hybrid Losses for Hierarchical Embedding Learning](https://mlsanity.com/paper/2501.12796/) (2025)
- [Automatic Music Sample Identification with Multi-Track Contrastive Learning](https://mlsanity.com/paper/2510.11507/) (2025)
- [Zero-shot Musical Stem Retrieval with Joint-Embedding Predictive Architectures](https://mlsanity.com/paper/2411.19806/) (2024)
- [Barwise Section Boundary Detection in Symbolic Music Using Convolutional Neural Networks](https://mlsanity.com/paper/2509.16566/) (2025)
- [Reflector: Arrangement-Aware Harmonic Retrieval for Sample-Based Composition](https://mlsanity.com/paper/2607.22413/) (2026)

## Abstract (arXiv)

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.