MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment

Dong, Hao-Wen; Hsiao, Wen-Yi; Yang, Li-Chia; Yang, Yi-Hsuan

doi:10.1609/aaai.v32i1.11312

Cited by 281 publications

(106 citation statements)

References 15 publications

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“…Yoon et al [23] also focus on the temporal dynamics of generated time series: By using a supervised loss, the proposed TimeGAN can better capture temporal dynamics. In addition to temporal structures, MuseGAN by Dong et al [6] considers the interplay of different instruments. For this purpose, they integrate multiple generators that focus on different music characteristics.…”

Section: Related Workmentioning

confidence: 99%

Controlling non-stationarity and periodicities in time series generation using conditional invertible neural networks

et al. 2022

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Generated synthetic time series aim to be both realistic by mirroring the characteristics of real-world time series and useful by including characteristics that are useful for subsequent applications, such as forecasting and missing value imputation. To generate such realistic and useful time series, we require generation methods capable of controlling the non-stationarity and periodicities of the generated time series. However, existing approaches do not consider such explicit control. Therefore, in the present paper, we present a novel approach to control non-stationarity and periodicities with calendar and statistical information when generating time series. We first define the requirements for methods to generate time series with non-stationarity and periodicities, which we show are not fulfilled by existing generation methods. Second, we formally describe the novel approach for controlling non-stationarity and periodicities in generated time series. Thirdly, we introduce an exemplary implementation of this approach using a conditional Invertible Neural Network (cINN). We evaluate this cINN empirically in experiments with real-world data sets and compare it to state-of-the-art time series generation methods. Our experiments show that the evaluated cINN can generate time series with controlled periodicities and non-stationarity, and it also generally outperforms the selected benchmarks.

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Section: Related Workmentioning

confidence: 99%

Controlling non-stationarity and periodicities in time series generation using conditional invertible neural networks

et al. 2022

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“…This method has checked the performance and analyzed it through the computational method. Dong et al discuss multi-track sequential generative adversarial networks for symbolic music generation and accompaniment called Musegan [ 12 ]. This work uses the generative adversarial networks (GANs) framework for music generation with the help of three models.…”

Section: Related Workmentioning

confidence: 99%

A Lightweight Deep Learning-Based Approach for Jazz Music Generation in MIDI Format

Yadav¹,

Khan²,

Singh

et al. 2022

Computational Intelligence and Neuroscience

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In today’s real-world, estimation of the level of difficulty of the musical is part of very meaningful musical learning. A musical learner cannot learn without a defined precise estimation. This problem is not very basic but it is complicated up to some extent because of the subjectivity of the contents and the scarcity of the data. In this paper, a lightweight model that generates original music content using deep learning along with generating music based on a specific genre is proposed. The paper discusses a lightweight deep learning-based approach for jazz music generation in MIDI format. In this work, the genre of music chosen is Jazz, and the songs selected are classical numbers composed by various artists. All the songs are in MIDI format and there might be differences in the pace or tone of the music. It is prudential to make sure that the chosen datasets that do not have these kinds of differences and are similar to the final output as desired. A model is trained to take in a part of a music file as input and should produce its continuation. The result generated should be similar to the dataset given as the input. Moreover, the proposed model also generates music using a particular instrument.

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“…In 2018, Dong. H et al proposed the MuseGAN network, which combined the convolutional neural network and adversarial generation network to generate multi-track music in the MIDI format [13]. In 2019, Zhang.…”

Section: Related Workmentioning

confidence: 99%

A transformer generative adversarial network for multi‐track music generation

Jin

Wang

et al. 2021

CAAI Trans on Intel Tech

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This study proposes a new generation network based on transformers and guided by the music theory to produce high‐quality music work. In this study, the decoding block of the transformer is used to learn the internal information of single‐track music, and cross‐track transformers are used to learn the information amongst the tracks of different musical instruments. A reward network based on the music theory is proposed, which optimizes the global and local loss objective functions while training and discriminating the network so that the reward network can provide a reliable adjustment method for the generation of the network. The method of combining the reward network and cross entropy loss is used to guide the training of the generator and produce high‐quality music work. Compared with other multi‐track music generation models, the experimental results verify the validity of the model.

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MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment

Cited by 281 publications

References 15 publications

Controlling non-stationarity and periodicities in time series generation using conditional invertible neural networks

Controlling non-stationarity and periodicities in time series generation using conditional invertible neural networks

A Lightweight Deep Learning-Based Approach for Jazz Music Generation in MIDI Format

A transformer generative adversarial network for multi‐track music generation

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