# Actions Speak Louder than Listening: Evaluating Music Style Transfer based on Editing Experience

arXiv: https://arxiv.org/abs/2110.12855 · HTML: https://mlsanity.com/paper/2110.12855/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 42 references that are in the map.
- First posted on arXiv 25 Oct 2021.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Pixel Recurrent Neural Networks](https://mlsanity.com/paper/1601.06759/) (2016)
- [Jukebox: A Generative Model for Music](https://mlsanity.com/paper/2005.00341/) (2020)
- [Stabilizing Transformers for Reinforcement Learning](https://mlsanity.com/paper/1910.06764/) (2019)
- [Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders](https://mlsanity.com/paper/1704.01279/) (2017)
- [Music Transformer](https://mlsanity.com/paper/1809.04281/) (2018)
- [A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music](https://mlsanity.com/paper/1803.05428/) (2018)
- [DeepBach: a Steerable Model for Bach Chorales Generation](https://mlsanity.com/paper/1612.01010/) (2016)
- [Counterpoint by Convolution](https://mlsanity.com/paper/1903.07227/) (2019)
- [Music transcription modelling and composition using deep learning](https://mlsanity.com/paper/1604.08723/) (2016)
- [Language Models with Transformers](https://mlsanity.com/paper/1904.09408/) (2019)
- [Encoding Musical Style with Transformer Autoencoders](https://mlsanity.com/paper/1912.05537/) (2019)
- [Symbolic Music Genre Transfer with CycleGAN](https://mlsanity.com/paper/1809.07575/) (2018)
- [AI Song Contest: Human-AI Co-Creation in Songwriting](https://mlsanity.com/paper/2010.05388/) (2020)
- [Automatic Melody Harmonization with Triad Chords: A Comparative Study](https://mlsanity.com/paper/2001.02360/) (2020)
- [Neural Style Transfer for Audio Spectograms](https://mlsanity.com/paper/1801.01589/) (2018)
- [Pop Music Transformer: Beat-based Modeling and Generation of Expressive Pop Piano Compositions](https://mlsanity.com/paper/2002.00212/) (2020)
- [Play as You Like: Timbre-enhanced Multi-modal Music Style Transfer](https://mlsanity.com/paper/1811.12214/) (2018)
- [Quantifying Musical Style: Ranking Symbolic Music based on Similarity to a Style](https://mlsanity.com/paper/2003.06226/) (2020)
- [CycleDRUMS: Automatic Drum Arrangement For Bass Lines Using CycleGAN](https://mlsanity.com/paper/2104.00353/) (2021)
- [Neural Translation of Musical Style](https://mlsanity.com/paper/1708.03535/) (2017)
- [Learning to Generate Music with BachProp](https://mlsanity.com/paper/1812.06669/) (2018)

## Abstract (arXiv)

The subjective evaluation of music generation techniques has been mostly done with questionnaire-based listening tests while ignoring the perspectives from music composition, arrangement, and soundtrack editing. In this paper, we propose an editing test to evaluate users' editing experience of music generation models in a systematic way. To do this, we design a new music style transfer model combining the non-chronological inference architecture, autoregressive models and the Transformer, which serves as an improvement from the baseline model on the same style transfer task. Then, we compare the performance of the two models with a conventional listening test and the proposed editing test, in which the quality of generated samples is assessed by the amount of effort (e.g., the number of required keyboard and mouse actions) spent by users to polish a music clip. Results on two target styles indicate that the improvement over the baseline model can be reflected by the editing test quantitatively. Also, the editing test provides profound insights which are not accessible from usual listening tests. The major contribution of this paper is the systematic presentation of the editing test and the corresponding insights, while the proposed music style transfer model based on state-of-the-art neural networks represents another contribution.