# Neural Loop Combiner: Neural Network Models for Assessing the Compatibility of Loops

arXiv: https://arxiv.org/abs/2008.02011 · HTML: https://mlsanity.com/paper/2008.02011/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2020.

## 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)
- [SampleMatch: Drum Sample Retrieval by Musical Context](https://mlsanity.com/paper/2208.01141/) (2022)
- [Stem-JEPA: A Joint-Embedding Predictive Architecture for Musical Stem Compatibility Estimation](https://mlsanity.com/paper/2408.02514/) (2024)
- [Modeling the Compatibility of Stem Tracks to Generate Music Mashups](https://mlsanity.com/paper/2103.14208/) (2021)
- [Multi-Genre Music Transformer -- Composing Full Length Musical Piece](https://mlsanity.com/paper/2301.02385/) (2023)
- [Zero-shot Musical Stem Retrieval with Joint-Embedding Predictive Architectures](https://mlsanity.com/paper/2411.19806/) (2024)
- [Learning Normal Patterns in Musical Loops](https://mlsanity.com/paper/2505.23784/) (2025)
- [Reflector: Arrangement-Aware Harmonic Retrieval for Sample-Based Composition](https://mlsanity.com/paper/2607.22413/) (2026)

## Abstract (arXiv)

Music producers who use loops may have access to thousands in loop libraries, but finding ones that are compatible is a time-consuming process; we hope to reduce this burden with automation. State-of-the-art systems for estimating compatibility, such as AutoMashUpper, are mostly rule-based and could be improved on with machine learn-ing. To train a model, we need a large set of loops with ground truth compatibility values. No such dataset exists, so we extract loops from existing music to obtain positive examples of compatible loops, and propose and compare various strategies for choosing negative examples. For re-producibility, we curate data from the Free Music Archive.Using this data, we investigate two types of model architectures for estimating the compatibility of loops: one based on a Siamese network, and the other a pure convolutional neural network (CNN). We conducted a user study in which participants rated the quality of the combinations suggested by each model, and found the CNN to outperform the Siamese network. Both model-based approaches outperformed the rule-based one. We have opened source the code for building the models and the dataset.