# LoopNet: Musical Loop Synthesis Conditioned On Intuitive Musical Parameters

arXiv: https://arxiv.org/abs/2105.10371 · HTML: https://mlsanity.com/paper/2105.10371/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 31 references that are in the map.
- First posted on arXiv 21 May 2021.

## Builds on (most cited first, 21 of 21)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders](https://mlsanity.com/paper/1704.01279/) (2017)
- [Adversarial Audio Synthesis](https://mlsanity.com/paper/1802.04208/) (2018)
- [Parallel WaveNet: Fast High-Fidelity Speech Synthesis](https://mlsanity.com/paper/1711.10433/) (2017)
- [DDSP: Differentiable Digital Signal Processing](https://mlsanity.com/paper/2001.04643/) (2020)
- [Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation](https://mlsanity.com/paper/1806.03185/) (2018)
- [GANSynth: Adversarial Neural Audio Synthesis](https://mlsanity.com/paper/1902.08710/) (2019)
- [SampleRNN: An Unconditional End-to-End Neural Audio Generation Model](https://mlsanity.com/paper/1612.07837/) (2016)
- [The challenge of realistic music generation: modelling raw audio at scale](https://mlsanity.com/paper/1806.10474/) (2018)
- [Deep Learning Techniques for Music Generation -- A Survey](https://mlsanity.com/paper/1709.01620/) (2017)
- [Neural source-filter waveform models for statistical parametric speech synthesis](https://mlsanity.com/paper/1904.12088/) (2019)
- [DrumGAN: Synthesis of Drum Sounds With Timbral Feature Conditioning Using Generative Adversarial Networks](https://mlsanity.com/paper/2008.12073/) (2020)
- [A Neural Parametric Singing Synthesizer](https://mlsanity.com/paper/1704.03809/) (2017)
- [WGANSing: A Multi-Voice Singing Voice Synthesizer Based on the Wasserstein-GAN](https://mlsanity.com/paper/1903.10729/) (2019)
- [Neural Percussive Synthesis Parameterised by High-Level Timbral Features](https://mlsanity.com/paper/1911.11853/) (2019)
- [Generating Albums with SampleRNN to Imitate Metal, Rock, and Punk Bands](https://mlsanity.com/paper/1811.06633/) (2018)
- [Data Efficient Voice Cloning for Neural Singing Synthesis](https://mlsanity.com/paper/1902.07292/) (2019)
- [Generating Black Metal and Math Rock: Beyond Bach, Beethoven, and Beatles](https://mlsanity.com/paper/1811.06639/) (2018)

## Built on by (most cited first, 3 of 3)

- [The Ethical Implications of Generative Audio Models: A Systematic Literature Review](https://mlsanity.com/paper/2307.05527/) (2023)
- [LoopGen: Training-Free Loopable Music Generation](https://mlsanity.com/paper/2504.04466/) (2025)
- [Pitch-Conditioned Instrument Sound Synthesis From an Interactive Timbre Latent Space](https://mlsanity.com/paper/2510.04339/) (2025)

## Abstract (arXiv)

Loops, seamlessly repeatable musical segments, are a cornerstone of modern music production. Contemporary artists often mix and match various sampled or pre-recorded loops based on musical criteria such as rhythm, harmony and timbral texture to create compositions. Taking such criteria into account, we present LoopNet, a feed-forward generative model for creating loops conditioned on intuitive parameters. We leverage Music Information Retrieval (MIR) models as well as a large collection of public loop samples in our study and use the Wave-U-Net architecture to map control parameters to audio. We also evaluate the quality of the generated audio and propose intuitive controls for composers to map the ideas in their minds to an audio loop.