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Frontiers of Information Technology & Electronic Engineering

ISSN 2095-9184 (print), ISSN 2095-9230 (online)

Suno: potential, prospects, and trends

Abstract: Suno has attracted wide attention due to its impressive capabilities. It demonstrates technological advancements and opens up new possibilities for music composition, representing a milestone in the development of artificial intelligence (AI) music generation. In this paper, we first introduce the background and summarize the general technical framework of AI music generation, followed by an analysis of Suno’s advantages and disadvantages. Finally, we discuss the future trends in Music and AI.

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Chinese Summary  <8> Suno:潜力、前景与趋势

俞佳兴1,吴宋若瑶1,卢冠廷1,李子晋2,周莉3,张克俊1,4
1浙江大学计算机科学与技术学院,中国杭州市,310027
2中央音乐学院音乐人工智能与音乐信息科技系,中国北京市,100031
3中国地质大学(武汉)艺术与传媒学院,中国武汉市,430074
4浙江大学长三角智慧绿洲创新中心,中国嘉兴市,314100
摘要:Suno因其出色的音乐生成能力受到广泛关注,其不仅展现了音乐人工智能技术的进步,也为音乐创作开辟了新的可能,是音乐人工智能生成发展的一个里程碑。本文介绍音乐人工智能生成的技术背景,总结音乐人工智能生成的通用技术框架,分析Suno的优势和局限,并讨论音乐人工智能的未来趋势。

关键词组:音乐人工智能;音乐生成;音乐人工智能生成平台;Suno


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References:

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[1]Agostinelli A, Denk TI, Borsos Z, et al., 2023. MusicLM: generating music from text. https://arxiv.org/abs/2301.11325

[2]Al-Rfou R, Choe D, Constant N, et al., 2019. Character-level language modeling with deeper self-attention. 33rd AAAI Conf on Artificial Intelligence, p.3159-3166.

[3]Ao JY, Wang R, Zhou L, et al., 2022. SpeechT5: unified-modal encoder-decoder pre-training for spoken language processing. Proc 60th Annual Meeting of the Association for Computational Linguistics, p.5723-5738.

[4]Brown TB, Mann B, Ryder N, et al., 2020. Language models are few-shot learners. Proc 34th Int Conf on Neural Information Processing Systems, Article 159.

[5]Coldewey D, 2022. Try Riffusion, an AI Model That Composes Music by Visualizing It. https://techcrunch.com/2022/12/15/try-riffusion-an-ai-model-that-composes-music-by-visualizing-it/ [Accessed on Apr. 6, 2024].

[6]Copet J, Kreuk F, Gat I, et al., 2023. Simple and controllable music generation. Proc 37th Int Conf on Neural Information Processing Systems, Article 2066.

[7]Dai ZH, Yang ZL, Yang YM, et al., 2019. Transformer-XL: attentive language models beyond a fixed-length context. Proc 57th Conf of the Association for Computational Linguistics, p.2978-2988.

[8]Dhariwal P, Jun H, Payne C, et al., 2020. Jukebox: a generative model for music. https://arxiv.org/abs/2005.00341

[9]Freyberg K, 2024. Introducing v3. https://www.suno.ai/blog/v3 [Accessed on Apr. 6, 2024].

[10]Hochreiter S, Schmidhuber J, 1997. Long short-term memory. Neur Comput, 9(8):1735-1780.

[11]Hsiao WY, Liu JY, Yeh YC, et al., 2021. Compound Word Transformer: learning to compose full-song music over dynamic directed hypergraphs. 35th AAAI Conf on Artificial Intelligence, p.178-186.

[12]Huang CZA, Vaswani A, Uszkoreit J, et al., 2019. Music Transformer: generating music with long-term structure. 7th Int Conf on Learning Representations.

[13]Huang QQ, Park DS, Wang T, et al., 2023. Noise2Music: text-conditioned music generation with diffusion models. https://arxiv.org/abs/2302.03917

[14]Huang YS, Yang YH, 2020. Pop Music Transformer: beat-based modeling and generation of expressive pop piano compositions. Proc 28th ACM Int Conf on Multimedia, p.1180-1188.

[15]Kreuk F, Synnaeve G, Polyak A, et al., 2023. AudioGen: textually guided audio generation. 11th Int Conf on Learning Representations.

[16]Liu HH, Chen ZH, Yuan Y, et al., 2023. AudioLDM: text-to-audio generation with latent diffusion models. Proc 40th Int Conf on Machine Learning, p.21450-21474.

[17]O’Boyle M, 2023. (Re)Discovering Music Theory: AI Algorithm Learns the Rules of Musical Composition and Provides a Framework for Knowledge Discovery. https://csl.illinois.edu/news-and-media/rediscovering-music-theory-ai-algorithm-learns-the-rules-of-musical-composition-and-provides-a-framework-for-knowledge-discovery [Accessed on Apr. 6, 2024].

[18]Ouyang L, Wu J, Jiang X, et al., 2022. Training language models to follow instructions with human feedback. Proc 36th Int Conf on Neural Information Processing Systems, Article 2011.

[19]Ren Y, He JZ, Tan X, et al., 2020. PopMAG: pop music accompaniment generation. Proc 28th ACM Int Conf on Multimedia, p.1198-1206.

[20]Ren Y, Hu CX, Tan X, et al., 2021. FastSpeech 2: fast and high-quality end-to-end text to speech. 9th Int Conf on Learning Representations.

[21]Touvron H, Martin L, Stone K, et al., 2023. Llama 2: open foundation and fine-tuned chat models. https://arxiv.org/abs/2307.09288

[22]Vaswani A, Shazeer N, Parmar N, et al., 2017. Attention is all you need. Proc 31st Int Conf on Neural Information Processing Systems, p.6000-6010.

[23]Wu J, Liu XG, Hu XL, et al., 2020. PopMNet: generating structured pop music melodies using neural networks. Artif Intell, 286:103303.

[24]Wu XD, Huang ZJ, Zhang KJ, et al., 2024. MelodyGLM: multi-task pre-training for symbolic melody generation. https://arxiv.org/abs/2309.10738

[25]Yu HZ, Varshney LR, Taube H, et al., 2022. (Re)Discovering laws of music theory using information lattice learning. IEEE BITS Inform Theory Mag, 2(1):58-75.

[26]Yuan RB, Lin HF, Wang Y, et al., 2024. ChatMusician: understanding and generating music intrinsically with LLM. https://arxiv.org/abs/2402.16153

[27]Zeng ML, Tan X, Wang R, et al., 2021. MusicBERT: symbolic music understanding with large-scale pre-training. Findings of the Association for Computational Linguistics, p.791-800.

[28]Zhou J, Ke P, Qiu XP, et al., 2023. ChatGPT: potential, prospects, and limitations. Front Inform Technol Electron Eng, early access.

[29]Zou Y, Zou P, Zhao Y, et al., 2022. MELONS: generating melody with long-term structure using transformers and structure graph. IEEE Int Conf on Acoustics, Speech and Signal Processing, p.191-195.

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DOI:

10.1631/FITEE.2400299

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On-line Access:

2024-08-27

Received:

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