https://github.com/carlosholivan/DeepLearningMusicGeneration
State of the Art of Music Generation with Deep Learning and AI
https://github.com/carlosholivan/DeepLearningMusicGeneration
deep-learning mir music-generation music-generation-deep-learning neural-network-architectures
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State of the Art of Music Generation with Deep Learning and AI
- Host: GitHub
- URL: https://github.com/carlosholivan/DeepLearningMusicGeneration
- Owner: carlosholivan
- License: apache-2.0
- Created: 2020-04-12T14:38:16.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2023-03-16T19:28:34.000Z (about 2 years ago)
- Last Synced: 2024-10-27T14:45:39.081Z (7 months ago)
- Topics: deep-learning, mir, music-generation, music-generation-deep-learning, neural-network-architectures
- Homepage: https://carlosholivan.github.io/DeepLearningMusicGeneration/
- Size: 16.8 MB
- Stars: 274
- Watchers: 12
- Forks: 25
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# DEEP LEARNING FOR MUSIC GENERATION
This repository is maintained by [**Carlos Hernández-Oliván**](https://carlosholivan.github.io/index.html)([email protected]) and it presents the State of the Art of Music Generation. Most of these references (previous to 2022) are included in the review paper ["Music Composition with Deep Learning: A Review"](#https://arxiv.org/abs/2108.12290). The authors of the paper want to thank Jürgen Schmidhuber for his suggestions.
[](./LICENSE)
Make a pull request if you want to contribute to this references list.
You can download a PDF version of this repo here: [README.pdf](AIMusicGeneration.pdf)
All the images belong to their corresponding authors.
## Table of Contents
1. [Algorithmic Composition](#algorithmic-composition)
- [1992](#1992alg)
- [Books](#books-alg)
2. [Neural Network Architectures](#neural-network-architectures)
3. [Deep Learning Models for Symbolic Music Generation](#deep-learning-music-generation)
- [2023](#2023deep)
- [2022](#2022deep)
- [2021](#2021deep)
- [2020](#2020deep)
- [2019](#2019deep)
- [2018](#2018deep)
- [2017](#2017deep)
- [2016](#2016deep)
- [2015](#2015deep)
- [2002](#2002deep)
- [1990s](#1990deep)
- [Books and Reviews](#books-reviews-deep)
- [Books](#books-deep)
- [Reviews](#reviews-deep)
4. [Deep Learning Models for Audio Music Generation](#deep-learning-audio-generation)
- [2023](#2023audiodeep)
- [2022](#2022audiodeep)
- [2021](#2021audiodeep)
- [2020](#2020audiodeep)
- [2017](#2017audiodeep)
5. [Datasets](#datasets)
6. [Journals and Conferences](#journals)
7. [Authors](#authors)
8. [Research Groups and Labs](#labs)
10. [Apps for Music Generation with AI](#apps)
11. [Other Resources](#other-resources)
## 2. Algorithmic Composition
### 1992
#### HARMONET
Hild, H., Feulner, J., & Menzel, W. (1992). HARMONET: A neural net for harmonizing chorales in the style of JS Bach. In Advances in neural information processing systems (pp. 267-274). [Paper](https://proceedings.neurips.cc/paper/1991/file/a7aeed74714116f3b292a982238f83d2-Paper.pdf)
### Books
* Westergaard, P. (1959). Experimental Music. Composition with an Electronic Computer.
* Todd, P. M. (1989). A connectionist approach to algorithmic composition. Computer Music Journal, 13(4), 27-43.
* Cope, D. (2000). The algorithmic composer (Vol. 16). AR Editions, Inc..
* Nierhaus, G. (2009). Algorithmic composition: paradigms of automated music generation. Springer Science & Business Media.
* Müller, M. (2015). Fundamentals of music processing: Audio, analysis, algorithms, applications. Springer.
* McLean, A., & Dean, R. T. (Eds.). (2018). The Oxford handbook of algorithmic music. Oxford University Press.
## 2. Neural Network Architectures
| NN Architecture | Year | Authors | Link to original paper | Slides |
| ------------- | ------------- | ------------- | ------------- | ------------- |
| Long Short-Term Memory (LSTM) | 1997 | Sepp Hochreiter, Jürgen Schmidhuber | http://www.bioinf.jku.at/publications/older/2604.pdf | [LSTM.pdf](Slides/LSTM_v1.pdf) |
| Convolutional Neural Network (CNN) | 1998 | Yann LeCun, Léon Bottou, YoshuaBengio, Patrick Haffner | http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf | |
| Variational Auto Encoder (VAE) | 2013 | Diederik P. Kingma, Max Welling | https://arxiv.org/pdf/1312.6114.pdf |
| Generative Adversarial Networks (GAN) | 2014 | Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio | https://arxiv.org/pdf/1406.2661.pdf | |
| Transformer | 2017 | Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin | https://arxiv.org/pdf/1706.03762.pdf | |
| Diffusion Models | 2015 | Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, Surya Ganguli | https://arxiv.org/abs/1503.03585 | |
## 3. Deep Learning Models for Music Generation
### 2023
#### RL-Chord
Ji, S., Yang, X., Luo, J., & Li, J. (2023). RL-Chord: CLSTM-Based Melody Harmonization Using Deep Reinforcement Learning. IEEE Transactions on Neural Networks and Learning Systems.
[Paper](https://ieeexplore.ieee.org/abstract/document/10063204)
#### FIGARO: Generating Symbolic Music with Fine-Grained Artistic Control
von Rütte, D., Biggio, L., Kilcher, Y., & Hoffman, T. (2022). FIGARO: Generating Symbolic Music with Fine-Grained Artistic Control. Accepted ICLR 2023.
[Paper](https://arxiv.org/abs/2201.10936)
### 2022
#### Museformer
Yu, B., Lu, P., Wang, R., Hu, W., Tan, X., Ye, W., ... & Liu, T. Y. (2022). Museformer: Transformer with Fine-and Coarse-Grained Attention for Music Generation. NIPS 2022.
[Paper](https://openreview.net/forum?id=GFiqdZOm-Ei) [NIPS Presentation](https://nips.cc/virtual/2022/poster/54604)
#### Bar Transformer
Qin, Y., Xie, H., Ding, S., Tan, B., Li, Y., Zhao, B., & Ye, M. (2022). Bar transformer: a hierarchical model for learning long-term structure and generating impressive pop music. Applied Intelligence, 1-19.
[Paper](https://link.springer.com/article/10.1007/s10489-022-04049-3)
#### Symphony Generation with Permutation Invariant Language Model
Liu, J., Dong, Y., Cheng, Z., Zhang, X., Li, X., Yu, F., & Sun, M. (2022). Symphony Generation with Permutation Invariant Language Model. arXiv preprint arXiv:2205.05448.
[Paper](http://128.84.4.34/abs/2205.05448) [Code](https://github.com/symphonynet/SymphonyNet) [Samples](https://symphonynet.github.io/)
#### Theme Transfomer
Shih, Y. J., Wu, S. L., Zalkow, F., Muller, M., & Yang, Y. H. (2022). Theme Transformer: Symbolic Music Generation with Theme-Conditioned Transformer. IEEE Transactions on Multimedia.
[Paper](https://arxiv.org/abs/2111.04093) [GitHub](https://github.com/atosystem/ThemeTransformer)
### 2021
#### Compound Word Transformer
Hsiao, W. Y., Liu, J. Y., Yeh, Y. C., & Yang, Y. H. (2021, May). Compound word transformer: Learning to compose full-song music over dynamic directed hypergraphs. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 1, pp. 178-186).
[Paper](https://ojs.aaai.org/index.php/AAAI/article/view/16091) [GitHub](https://github.com/YatingMusic/compound-word-transformer)
#### Melody Generation from Lyrics
Yu, Y., Srivastava, A., & Canales, S. (2021). Conditional lstm-gan for melody generation from lyrics. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 17(1), 1-20.
[Paper](https://dl.acm.org/doi/abs/10.1145/3424116)
#### Music Generation with Diffusion Models
Mittal, G., Engel, J., Hawthorne, C., & Simon, I. (2021). Symbolic music generation with diffusion models. arXiv preprint arXiv:2103.16091.
[Paper](https://arxiv.org/abs/2103.16091) [GitHub](https://github.com/magenta/symbolic-music-diffusion)
### 2020
#### Pop Musc Transfomer
Huang, Y. S., & Yang, Y. H. (2020, October). Pop music transformer: Beat-based modeling and generation of expressive pop piano compositions. In Proceedings of the 28th ACM International Conference on Multimedia (pp. 1180-1188).
[Paper](https://dl.acm.org/doi/abs/10.1145/3394171.3413671) [GitHub](https://github.com/YatingMusic/remi)
#### Controllable Polyphonic Music Generation
Wang, Z., Wang, D., Zhang, Y., & Xia, G. (2020). Learning interpretable representation for controllable polyphonic music generation. arXiv preprint arXiv:2008.07122.
[Paper](https://arxiv.org/abs/2008.07122) [Web](https://program.ismir2020.net/poster_5-05.html) [Video](https://www.youtube.com/watch?v=Sb6jXP_7dtE&t=28s&ab_channel=ISMIR2020)
#### MMM: Multitrack Music Generation
Ens, J., & Pasquier, P. (2020). Mmm: Exploring conditional multi-track music generation with the transformer. arXiv preprint arXiv:2008.06048.
[Paper](https://arxiv.org/abs/2008.06048) [Web](https://jeffreyjohnens.github.io/MMM/) [Colab](https://colab.research.google.com/drive/1xGZW3GP24HUsxnbebqfy1iCyYySQ64Vs?usp=sharing) [Github (AI Guru)](https://github.com/AI-Guru/MMM-JSB)
#### Transformer-XL
Wu, X., Wang, C., & Lei, Q. (2020). Transformer-XL Based Music Generation with Multiple Sequences of Time-valued Notes. arXiv preprint arXiv:2007.07244.
[Paper](https://arxiv.org/abs/2007.07244)
#### Transformer VAE
Jiang, J., Xia, G. G., Carlton, D. B., Anderson, C. N., & Miyakawa, R. H. (2020, May). Transformer vae: A hierarchical model for structure-aware and interpretable music representation learning. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 516-520). IEEE.
[Paper](https://ieeexplore.ieee.org/document/9054554)
### 2019
#### TonicNet
Peracha, O. (2019). Improving polyphonic music models with feature-rich encoding. arXiv preprint arXiv:1911.11775.
[Paper](https://arxiv.org/abs/1911.11775)
#### LakhNES
Donahue, C., Mao, H. H., Li, Y. E., Cottrell, G. W., & McAuley, J. (2019). LakhNES: Improving multi-instrumental music generation with cross-domain pre-training. arXiv preprint arXiv:1907.04868.
[Paper](https://arxiv.org/abs/1907.04868)
#### R-Transformer
Wang, Z., Ma, Y., Liu, Z., & Tang, J. (2019). R-transformer: Recurrent neural network enhanced transformer. arXiv preprint arXiv:1907.05572.
[Paper](https://arxiv.org/abs/1907.05572)
#### Maia Music Generator
[Web](https://maia.music.blog/2019/05/13/maia-a-new-music-generator/)
#### Coconet: Counterpoint by Convolution
Huang, C. Z. A., Cooijmans, T., Roberts, A., Courville, A., & Eck, D. (2019). Counterpoint by convolution. arXiv preprint arXiv:1903.07227.
[Paper](https://arxiv.org/abs/1903.07227) [Web](https://coconets.github.io/)
### 2018
#### Music Transformer - Google Magenta
Huang, C. Z. A., Vaswani, A., Uszkoreit, J., Shazeer, N., Simon, I., Hawthorne, et al. (2018). Music transformer. arXiv preprint arXiv:1809.04281.
[Web](https://magenta.tensorflow.org/music-transformer) [Poster](Images/transformer_poster.jpg) [Paper](https://arxiv.org/pdf/1809.04281.pdf)
#### Imposing Higher-level Structure in Polyphonic Music
Lattner, S., Grachten, M., & Widmer, G. (2018). Imposing higher-level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints. Journal of Creative Music Systems, 2, 1-31.
[Paper](https://arxiv.org/pdf/1612.04742.pdf)
#### MusicVAE - Google Magenta
Roberts, A., Engel, J., Raffel, C., Hawthorne, C., & Eck, D. (2018, July). A hierarchical latent vector model for learning long-term structure in music. In International Conference on Machine Learning (pp. 4364-4373). PMLR.
[Web](https://magenta.tensorflow.org/music-vae) [Paper](https://arxiv.org/pdf/1803.05428.pdf) [Code](https://github.com/tensorflow/magenta/tree/master/magenta/models/music_vae) [Google Colab](https://colab.research.google.com/notebooks/magenta/music_vae/music_vae.ipynb) [Explanation](https://medium.com/@musicvaeubcse/musicvae-understanding-of-the-googles-work-for-interpolating-two-music-sequences-621dcbfa307c)
### 2017
#### MorpheuS
Herremans, D., & Chew, E. (2017). MorpheuS: generating structured music with constrained patterns and tension. IEEE Transactions on Affective Computing, 10(4), 510-523.
[Paper](https://arxiv.org/pdf/1812.04832.pdf)
#### Polyphonic GAN
Lee, S. G., Hwang, U., Min, S., & Yoon, S. (2017). Polyphonic music generation with sequence generative adversarial networks. arXiv preprint arXiv:1710.11418.
[Paper](https://arxiv.org/abs/1710.11418)
#### BachBot - Microsoft
Liang, F. T., Gotham, M., Johnson, M., & Shotton, J. (2017, October). Automatic Stylistic Composition of Bach Chorales with Deep LSTM. In ISMIR (pp. 449-456).
[Paper](https://www.microsoft.com/en-us/research/publication/automatic-stylistic-composition-of-bach-chorales-with-deep-lstm/) [Liang Master Thesis 2016](https://www.mlmi.eng.cam.ac.uk/files/feynman_liang_8224771_assignsubmission_file_liangfeynmanthesis.pdf)
#### MuseGAN
Dong, H. W., Hsiao, W. Y., Yang, L. C., & Yang, Y. H. (2018, April). Musegan: Multi-track sequential generative adversarial networks for symbolic music generation and accompaniment. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 32, No. 1).
[Web](https://salu133445.github.io/musegan/) [Paper](https://arxiv.org/pdf/1709.06298.pdf) [Poster](Images/musegan_ismir2017.jpg) [GitHub](https://github.com/salu133445/musegan)
#### Composing Music with LSTM
Johnson, D. D. (2017, April). Generating polyphonic music using tied parallel networks. In International conference on evolutionary and biologically inspired music and art (pp. 128-143). Springer, Cham.
[Paper](https://link.springer.com/chapter/10.1007/978-3-319-55750-2_9) [Web](https://www.danieldjohnson.com/2015/08/03/composing-music-with-recurrent-neural-networks/) [GitHub](https://github.com/danieldjohnson/biaxial-rnn-music-composition) [Blog](https://www.danieldjohnson.com/2015/08/03/composing-music-with-recurrent-neural-networks/)
#### ORGAN
Guimaraes, G. L., Sanchez-Lengeling, B., Outeiral, C., Farias, P. L. C., & Aspuru-Guzik, A. (2017). Objective-reinforced generative adversarial networks (ORGAN) for sequence generation models. arXiv preprint arXiv:1705.10843.
[Paper](https://arxiv.org/abs/1705.10843)
#### MidiNet
Yang, L. C., Chou, S. Y., & Yang, Y. H. (2017). MidiNet: A convolutional generative adversarial network for symbolic-domain music generation. arXiv preprint arXiv:1703.10847.
[Paper](https://arxiv.org/abs/1703.10847)
### 2016
#### DeepBach
Hadjeres, G., Pachet, F., & Nielsen, F. (2017, July). Deepbach: a steerable model for bach chorales generation. In International Conference on Machine Learning (pp. 1362-1371). PMLR.
[Web](http://www.flow-machines.com/history/projects/deepbach-polyphonic-music-generation-bach-chorales/) [Paper](https://arxiv.org/pdf/1612.01010.pdf) [Code](https://github.com/Ghadjeres/DeepBach)
#### Fine-Tuning with RL
Jaques, N., Gu, S., Turner, R. E., & Eck, D. (2016). Generating music by fine-tuning recurrent neural networks with reinforcement learning.
[Paper](https://research.google/pubs/pub45871/)
#### C-RNN-GAN
Mogren, O. (2016). C-RNN-GAN: Continuous recurrent neural networks with adversarial training. arXiv preprint arXiv:1611.09904.
[Paper](https://arxiv.org/abs/1611.09904)
#### SeqGAN
Yu, L., Zhang, W., Wang, J., & Yu, Y. (2017, February). Seqgan: Sequence generative adversarial nets with policy gradient. In Proceedings of the AAAI conference on artificial intelligence (Vol. 31, No. 1).
[Paper](https://arxiv.org/abs/1609.05473)
### 2002
#### Temporal Structure in Music
Eck, D., & Schmidhuber, J. (2002, September). Finding temporal structure in music: Blues improvisation with LSTM recurrent networks. In Proceedings of the 12th IEEE workshop on neural networks for signal processing (pp. 747-756). IEEE.
[Paper](https://ieeexplore.ieee.org/document/1030094)
### 1980s - 1990s
Mozer, M. C. (1994). Neural network music composition by prediction: Exploring the benefits of psychoacoustic constraints and multi-scale processing. Connection Science, 6(2-3), 247-280.
[Paper](https://www.tandfonline.com/doi/abs/10.1080/09540099408915726)
### Books and Reviews
### Books
* Briot, J. P., Hadjeres, G., & Pachet, F. (2020). Deep learning techniques for music generation (pp. 1-249). Springer.
### Reviews
* Hernandez-Olivan, C., & Beltran, J. R. (2021). Music composition with deep learning: A review. arXiv preprint arXiv:2108.12290.
[Paper](https://arxiv.org/abs/2108.12290)
* Ji, S., Luo, J., & Yang, X. (2020). A Comprehensive Survey on Deep Music Generation: Multi-level Representations, Algorithms, Evaluations, and Future Directions. arXiv preprint arXiv:2011.06801.
[Paper](https://arxiv.org/abs/2011.06801)
* Briot, J. P., Hadjeres, G., & Pachet, F. D. (2017). Deep learning techniques for music generation--a survey. arXiv preprint arXiv:1709.01620.
[Paper](https://arxiv.org/abs/1709.01620)
## 4. Audio Generation
### 2023
#### Vall-E X
Zhang, Z., Zhou, L., Wang, C., Chen, S., Wu, Y., Liu, S., ... & Wei, F. (2023). Speak Foreign Languages with Your Own Voice: Cross-Lingual Neural Codec Language Modeling. arXiv preprint arXiv:2303.03926.
[Paper](https://arxiv.org/abs/2303.03926)
#### ERNIE Music
Zhu, P., Pang, C., Wang, S., Chai, Y., Sun, Y., Tian, H., & Wu, H. (2023). ERNIE-Music: Text-to-Waveform Music Generation with Diffusion Models. arXiv preprint arXiv:2302.04456.
[Paper](https://arxiv.org/abs/2302.04456)
#### Multi-Source Diffusion Models
Mariani, G., Tallini, I., Postolache, E., Mancusi, M., Cosmo, L., & Rodolà, E. (2023). Multi-Source Diffusion Models for Simultaneous Music Generation and Separation. arXiv preprint arXiv:2302.02257.
[Paper](https://arxiv.org/abs/2302.02257) [Samples](https://gladia-research-group.github.io/multi-source-diffusion-models/)
#### SingSong
Donahue, C., Caillon, A., Roberts, A., Manilow, E., Esling, P., Agostinelli, A., ... & Engel, J. (2023). SingSong: Generating musical accompaniments from singing. arXiv preprint arXiv:2301.12662.
[Paper](https://arxiv.org/abs/2301.12662) [Samples](https://storage.googleapis.com/sing-song/index.html)
#### AudioLDM
Liu, H., Chen, Z., Yuan, Y., Mei, X., Liu, X., Mandic, D., ... & Plumbley, M. D. (2023). AudioLDM: Text-to-Audio Generation with Latent Diffusion Models. arXiv preprint arXiv:2301.12503.
[Paper](https://arxiv.org/abs/2301.12503) [Samples](https://audioldm.github.io/) [GitHub] (https://github.com/haoheliu/AudioLDM)
#### Mousai
Schneider, F., Jin, Z., & Schölkopf, B. (2023). Mo\^ usai: Text-to-Music Generation with Long-Context Latent Diffusion. arXiv preprint arXiv:2301.11757.
[Paper](https://arxiv.org/abs/2301.11757)
#### Make-An-Audio
Huang, R., Huang, J., Yang, D., Ren, Y., Liu, L., Li, M., ... & Zhao, Z. (2023). Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models. arXiv preprint arXiv:2301.12661.
[Paper](https://arxiv.org/abs/2301.12661) [Samples](https://text-to-audio.github.io/)
#### Noise2Music
Huang, Q., Park, D. S., Wang, T., Denk, T. I., Ly, A., Chen, N., ... & Han, W. (2023). Noise2Music: Text-conditioned Music Generation with Diffusion Models. arXiv preprint arXiv:2302.03917.
[Paper](https://arxiv.org/abs/2302.03917) [Samples](https://google-research.github.io/noise2music/)
#### Msanii
Maina, K. (2023). Msanii: High Fidelity Music Synthesis on a Shoestring Budget. arXiv preprint arXiv:2301.06468.
[Paper](https://arxiv.org/abs/2301.06468)
#### MusicLM
Agostinelli, A., Denk, T. I., Borsos, Z., Engel, J., Verzetti, M., Caillon, A., ... & Frank, C. (2023). Musiclm: Generating music from text. arXiv preprint arXiv:2301.11325.
[Paper](https://arxiv.org/abs/2301.11325) [Samples](https://google-research.github.io/seanet/musiclm/examples/) [Dataset](https://www.kaggle.com/datasets/googleai/musiccaps)
### 2022
#### Musika
Pasini, M., & Schlüter, J. (2022). Musika! Fast Infinite Waveform Music Generation. arXiv preprint arXiv:2208.08706.
[Paper](https://arxiv.org/abs/2208.08706)
#### AudioLM
Borsos, Z., Marinier, R., Vincent, D., Kharitonov, E., Pietquin, O., Sharifi, M., ... & Zeghidour, N. (2022). Audiolm: a language modeling approach to audio generation. arXiv preprint arXiv:2209.03143.
[Paper](https://arxiv.org/abs/2209.03143) [Samples](https://google-research.github.io/seanet/audiolm/examples/)
### 2021
#### RAVE
Caillon, A., & Esling, P. (2021). RAVE: A variational autoencoder for fast and high-quality neural audio synthesis. arXiv preprint arXiv:2111.05011.
[Paper](https://arxiv.org/abs/2111.05011) [GitHub](https://github.com/acids-ircam/RAVE)
### 2020
#### Jukebox - OpenAI
[Web](https://openai.com/blog/jukebox/) [Paper](https://arxiv.org/abs/2005.00341) [GitHub](https://github.com/openai/jukebox/)
### 2017
#### MuseNet - OpenAI
[Web](https://openai.com/blog/musenet/)
## 5. Datasets
* JSB Chorales Dataset [Web](http://www-ens.iro.umontreal.ca/~boulanni/icml2012)
* Maestro Dataset [Web](https://magenta.tensorflow.org/datasets/maestro)
* The Lakh MIDI Dataset v0.1 [Web](https://colinraffel.com/projects/lmd/) [Tutorial IPython](https://nbviewer.jupyter.org/github/craffel/midi-dataset/blob/master/Tutorial.ipynb)
* MetaMIDI Dataset [Web](https://metacreation.net/metamidi-dataset/) [Zenodo](https://zenodo.org/record/5142664)
## 6. Journals and Conferences
* International Society for Music Information Retrieval (ISMIR) [Web](https://www.ismir.net/)
* IEEE Signal Processing (ICASSP) [Web](https://signalprocessingsociety.org/publications-resources)
* ELSEVIER Signal Processing Journal [Web](https://www.journals.elsevier.com/signal-processing)
* Association for the Advancement of Artificial Intelligence (AAAI) [Web](https://www.aaai.org/)
* Journal of Artificial Intelligence Research (JAIR) [Web](https://www.jair.org/index.php/jair)
* International Joint Conferences on Artificial Intelligence (IJCAI) [Web](https://www.ijcai.org/)
* International Conference on Learning Representations (ICLR) [Web](https://iclr.cc)
* IET Signal Processing Journal [Web](https://digital-library.theiet.org/content/journals/iet-spr)
* Journal of New Music Research (JNMR) [Web](https://www.tandfonline.com/loi/nnmr20)
* Audio Engineering Society - Conference on Semantic Audio (AES) [Web](http://www.aes.org/)
* International Conference on Digital Audio Effects (DAFx) [Web](http://dafx.de/)
## 7. Authors
* David Cope [Web](http://artsites.ucsc.edu/faculty/cope/)
* Colin Raffel [Web](https://colinraffel.com/)
* Jesse Engel [Web](https://jesseengel.github.io/)
* Douglas Eck [Web](http://www.iro.umontreal.ca/~eckdoug/)
* Anna Huang [Web](https://mila.quebec/en/person/anna-huang/)
* François Pachet [Web](https://www.francoispachet.fr/)
* Jeff Ens [Web](https://jeffens.com/)
* Philippe Pasquier [Web](https://www.sfu.ca/siat/people/research-faculty/philippe-pasquier.html)
## 8. Research Groups and Labs
* Google Magenta [Web](https://magenta.tensorflow.org/)
* Audiolabs Erlangen [Web](https://www.audiolabs-erlangen.de/)
* Music Informatics Group [Web](https://musicinformatics.gatech.edu/)
* Music and Artificial Intelligence Lab [Web](https://musicai.citi.sinica.edu.tw/)
* Metacreation Lab [Web](https://metacreation.net/)
## 9. Apps for Music Generation with AI
* AIVA (paid) [Web](https://www.aiva.ai/)
* Amper Music (paid) [Web](https://www.ampermusic.com/)
* Ecrett Music (paid) [Web](https://ecrettmusic.com/)
* Humtap (free, iOS) [Web](https://www.humtap.com/)
* Amadeus Code (free/paid, iOS) [Web](https://amadeuscode.com/top)
* Computoser (free) [Web](computoser.com)
* Brain.fm (paid) [Web](https://www.brain.fm/login?next=/app/player)
## 10. Other Resources
* Bustena (web in spanish to learn harmony theory) [Web](http://www.bustena.com/curso-de-armonia-i/)