# Adversarial Unsupervised Domain Adaptation for Harmonic-Percussive Source Separation

arXiv: https://arxiv.org/abs/2101.00701 · HTML: https://mlsanity.com/paper/2101.00701/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 40 references that are in the map.
- First posted on arXiv 3 Jan 2021.

## Builds on (most cited first, 16 of 16)

- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Moment Matching for Multi-Source Domain Adaptation](https://mlsanity.com/paper/1812.01754/) (2018)
- [Transferring GANs: generating images from limited data](https://mlsanity.com/paper/1805.01677/) (2018)
- [Cutting Music Source Separation Some Slakh: A Dataset to Study the Impact of Training Data Quality and Quantity](https://mlsanity.com/paper/1909.08494/) (2019)
- [MMDenseLSTM: An efficient combination of convolutional and recurrent neural networks for audio source separation](https://mlsanity.com/paper/1805.02410/) (2018)
- [Adversarial Semi-Supervised Audio Source Separation applied to Singing Voice Extraction](https://mlsanity.com/paper/1711.00048/) (2017)
- [Unsupervised adversarial domain adaptation for acoustic scene classification](https://mlsanity.com/paper/1808.05777/) (2018)
- [Adversarial Feature-Mapping for Speech Enhancement](https://mlsanity.com/paper/1809.02251/) (2018)
- [Building Corpora for Single-Channel Speech Separation Across Multiple Domains](https://mlsanity.com/paper/1811.02641/) (2018)
- [Improving singing voice separation using Deep U-Net and Wave-U-Net with data augmentation](https://mlsanity.com/paper/1903.01415/) (2019)
- [CASS: Cross Adversarial Source Separation via Autoencoder](https://mlsanity.com/paper/1905.09877/) (2019)
- [SVSGAN: Singing Voice Separation via Generative Adversarial Network](https://mlsanity.com/paper/1710.11428/) (2017)
- [TLR: Transfer Latent Representation for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1808.06206/) (2018)
- [Investigating kernel shapes and skip connections for deep learning-based harmonic-percussive separation](https://mlsanity.com/paper/1905.01899/) (2019)
- [Harmonic-Percussive Source Separation with Deep Neural Networks and Phase Recovery](https://mlsanity.com/paper/1807.11298/) (2018)

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

- [Annotation-free Automatic Music Transcription with Scalable Synthetic Data and Adversarial Domain Confusion](https://mlsanity.com/paper/2312.10402/) (2023)

## Abstract (arXiv)

This paper addresses the problem of domain adaptation for the task of music source separation. Using datasets from two different domains, we compare the performance of a deep learning-based harmonic-percussive source separation model under different training scenarios, including supervised joint training using data from both domains and pre-training in one domain with fine-tuning in another. We propose an adversarial unsupervised domain adaptation approach suitable for the case where no labelled data (ground-truth source signals) from a target domain is available. By leveraging unlabelled data (only mixtures) from this domain, experiments show that our framework can improve separation performance on the new domain without losing any considerable performance on the original domain. The paper also introduces the Tap & Fiddle dataset, a dataset containing recordings of Scandinavian fiddle tunes along with isolated tracks for 'foot-tapping' and 'violin'.