# Investigating the Performance and Energy Costs of Replicating Band-Split RNN for Music Source Separation

arXiv: https://arxiv.org/abs/2609.21918 · HTML: https://mlsanity.com/paper/2609.21918/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2026.

## Built on by (most cited first, 1 of 1)

- [Exposing the Cost of Deep Learning Audio Development](https://mlsanity.com/paper/2610.01619/) (2026)

## Abstract (arXiv)

Band-split recurrent neural network (BSRNN) is a popular music source separation model that yields close to state-of-the-art results using reasonable computational resources and public datasets. It is therefore interesting from a reproducible research perspective, but achieving its performance is not straightforward since its full code is not available. In this paper, we conduct a replication of BSRNN via implementing the full pipeline. We extend the original paper's analysis by experimentally studying various design choices about data preprocessing, the optimization protocol, and architectural parameters. We report and discuss this project's energy cost, and we underline how its footprint could have been substantial lower upon availability of the full pipeline, which advocates for more reproducible research practices. To comply with this objective, we publicly release our code and pre-trained models.