# Generating Albums with SampleRNN to Imitate Metal, Rock, and Punk Bands

arXiv: https://arxiv.org/abs/1811.06633 · HTML: https://mlsanity.com/paper/1811.06633/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2018.

## Built on by (most cited first, 5 of 5)

- [Explainability Paths for Sustained Artistic Practice with AI](https://mlsanity.com/paper/2407.15216/) (2024)
- [LoopNet: Musical Loop Synthesis Conditioned On Intuitive Musical Parameters](https://mlsanity.com/paper/2105.10371/) (2021)
- [Neural Network Layer Algebra: A Framework to Measure Capacity and Compression in Deep Learning](https://mlsanity.com/paper/2107.01081/) (2021)
- [Evaluating Deep Music Generation Methods Using Data Augmentation](https://mlsanity.com/paper/2201.00052/) (2021)
- [If Turing played piano with an artificial partner](https://mlsanity.com/paper/2402.08690/) (2024)

## Abstract (arXiv)

This early example of neural synthesis is a proof-of-concept for how machine learning can drive new types of music software. Creating music can be as simple as specifying a set of music influences on which a model trains. We demonstrate a method for generating albums that imitate bands in experimental music genres previously unrealized by traditional synthesis techniques (e.g. additive, subtractive, FM, granular, concatenative). Raw audio is generated autoregressively in the time-domain using an unconditional SampleRNN. We create six albums this way. Artwork and song titles are also generated using materials from the original artists' back catalog as training data. We try a fully-automated method and a human-curated method. We discuss its potential for machine-assisted production.