{"type":"ScholarlyArticle","title":"InstructME: An Instruction Guided Music Edit Framework with Latent Diffusion Models","authors":[{"name":"Bing Han","slug":"bing-han","given":"Bing","family":"Han","affiliation":"Auditory Cognition and Computational Acoustics Lab , Shanghai Jiao Tong University"},{"name":"Junyu Dai","slug":"junyu-dai","given":"Junyu","family":"Dai","affiliation":"ByteDance"},{"name":"Weituo Hao","slug":"weituo-hao","given":"Weituo","family":"Hao","affiliation":"ByteDance"},{"name":"Xinyan He","slug":"xinyan-he","given":"Xinyan","family":"He","affiliation":"ByteDance"},{"name":"Dong Guo","slug":"dong-guo","given":"Dong","family":"Guo","affiliation":"ByteDance"},{"name":"Jitong Chen","slug":"jitong-chen","given":"Jitong","family":"Chen","affiliation":"ByteDance"},{"name":"Yuxuan Wang","slug":"yuxuan-wang","given":"Yuxuan","family":"Wang","affiliation":"ByteDance"},{"name":"Yanmin Qian","slug":"yanmin-qian","given":"Yanmin","family":"Qian","affiliation":"Auditory Cognition and Computational Acoustics Lab , Shanghai Jiao Tong University"},{"name":"Xuchen Song","slug":"xuchen-song","given":"Xuchen","family":"Song","affiliation":"ByteDance"}],"abstract":"Music editing primarily entails the modification of instrument tracks or remixing in the whole, which offers a novel reinterpretation of the original piece through a series of operations. These music processing methods hold immense potential across various applications but demand substantial expertise. Prior methodologies, although effective for image and audio modifications, falter when directly applied to music. This is attributed to music's distinctive data nature, where such methods can inadvertently compromise the intrinsic harmony and coherence of music. In this paper, we develop InstructME, an Instruction guided Music Editing and remixing framework based on latent diffusion models. Our framework fortifies the U-Net with multi-scale aggregation in order to maintain consistency before and after editing. In addition, we introduce chord progression matrix as condition information and incorporate it in the semantic space to improve melodic harmony while editing. For accommodating extended musical pieces, InstructME employs a chunk transformer, enabling it to discern long-term temporal dependencies within music sequences. We tested InstructME in instrument-editing, remixing, and multi-round editing. Both subjective and objective evaluations indicate that our proposed method significantly surpasses preceding systems in music quality, text relevance and harmony.\r\nDemo samples are available at https://musicedit.github.io","doi":"10.24963/ijcai.2024/645","url":"https://ijcai.org/proceedings/2024/645","pdf":"https://ijcai.org/proceedings/2024/0645.pdf","markdown":"https://ijcai.org/proceedings/2024/645.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/645","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":645,"pages":{"first":5835,"last":5843},"keywords":["Multidisciplinary Topics and Applications: MTA: Arts and creativity","Multidisciplinary Topics and Applications: MTA: Other"],"videos":[],"references":[{"position":1,"raw":"[Agostinelli et al., 2023] Andrea Agostinelli, Timo I Denk, Zalán Borsos, Jesse Engel, Mauro Verzetti, Antoine Cail- lon, Qingqing Huang, Aren Jansen, Adam Roberts, Marco Tagliasacchi, et al. Musiclm: Generating music from text. arXiv preprint arXiv:2301.11325, 2023.","title":"Musiclm: Generating music from text","authors":"Andrea Agostinelli, I Timo, Zalán Denk, Jesse Borsos, Mauro Engel, Antoine Verzetti, Qingqing Caillon, Aren Huang, Adam Jansen, Marco Roberts, Tagliasacchi","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Cheuk et al., 2022] Kin Wai Cheuk, Keunwoo Choi, Qi- uqiang Kong, Bochen Li, Minz Won, Amy Hung, Ju- Chiang Wang, and Dorien Herremans. Jointist: Joint learn- ing for multi-instrument transcription and its applications. arXiv preprint arXiv:2206.10805, 2022.","title":"Jointist: Joint learning for multi-instrument transcription and its applications","authors":"Kin Wai Cheuk, Keunwoo Choi, Qiuqiang Kong, Bochen Li, Minz Won, Amy Hung, Ju-Chiang Wang, Dorien Herremans","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Copet et al., 2023] Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, and Alexandre Défossez. Simple and controllable music gen- eration. arXiv preprint arXiv:2306.05284, 2023.","title":"Simple and controllable music generation","authors":"Copet","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Dhariwal and Nichol, 2021] Prafulla Dhariwal and Alexan- der Nichol. Diffusion models beat gans on image synthe- sis. Advances in neural information processing systems, 34:8780-8794, 2021.","title":"Diffusion models beat gans on image synthesis","authors":"Nichol Dhariwal","year":"2021","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Donahue et al., 2023] Chris Donahue, Antoine Caillon, Adam Roberts, Ethan Manilow, Philippe Esling, An- drea Agostinelli, Mauro Verzetti, Ian Simon, Olivier Pietquin, Neil Zeghidour, et al. Singsong: Generating musical accompaniments from singing. arXiv preprint arXiv:2301.12662, 2023.","title":"Singsong: Generating musical accompaniments from singing","authors":"Chris Donahue, Antoine Caillon, Adam Roberts, Ethan Manilow, Philippe Esling, Andrea Agostinelli, Mauro Verzetti, Ian Simon, Olivier Pietquin, Neil Zeghidour","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Fagerjord, 2010] Anders Fagerjord. After convergence: Youtube and remix culture. International handbook of in- ternet research, pages 187-200, 2010.","title":"After convergence: Youtube and remix culture","authors":"Anders Fagerjord","year":"2010","venue":"International handbook of internet research","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Hershey et al., 2017] Shawn Hershey, Sourish Chaudhuri, Daniel PW Ellis, Jort F Gemmeke, Aren Jansen, R Chan- ning Moore, Manoj Plakal, Devin Platt, Rif A Saurous, Bryan Seybold, et al. Cnn architectures for large-scale au- dio classification. In 2017 ieee international conference on acoustics, speech and signal processing (icassp), pages 131-135. IEEE, 2017.","title":"Cnn architectures for large-scale audio classification","authors":"Shawn Hershey, Sourish Chaudhuri, P W Daniel, Ellis, Aren Jort F Gemmeke, R Channing Jansen, Manoj Moore, Devin Plakal, Rif A Platt, Bryan Saurous, Seybold","year":"2017","venue":"ieee international conference on acoustics, speech and signal processing (icassp)","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Hertz et al., 2022] Amir Hertz, Ron Mokady, Jay Tenen- baum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Prompt-to-prompt image editing with cross attention con- trol. arXiv preprint arXiv:2208.01626, 2022.","title":"Prompt-to-prompt image editing with cross attention control","authors":"Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, Daniel Cohen-Or","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Ho and Salimans, 2022] Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598, 2022.","title":"Classifier-free diffusion guidance","authors":"Jonathan Ho, Tim Salimans","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Ho et al., 2020] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020.","title":"Denoising diffusion probabilistic models","authors":"Jonathan Ho, Ajay Jain, Pieter Abbeel","year":"2020","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Holz, 2023] David Holz. Midjourney. artificial intelligence platform. accessible at https://www.midjourney.com. https://www.midjourney.com/, 2023. Accessed: 2023-07- 31.","title":"Midjourney. artificial intelligence platform","authors":"David Holz","year":"2023","venue":null,"doi":null,"url":"https://www.midjourney.com/,2023","internal_url":null},{"position":12,"raw":"[Huang et al., 2022] Qingqing Huang, Aren Jansen, Joon- seok Lee, Ravi Ganti, Judith Yue Li, and Daniel PW El- lis. Mulan: A joint embedding of music audio and natural language. arXiv preprint arXiv:2208.12415, 2022.","title":"Mulan: A joint embedding of music audio and natural language","authors":"Qingqing Huang, Aren Jansen, Joonseok Lee, Ravi Ganti, Judith Yue Li, Daniel Pw Ellis","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Huang et al., 2023a] Qingqing Huang, Daniel S Park, Tao Wang, Timo I Denk, Andy Ly, Nanxin Chen, Zhengdong Zhang, Zhishuai Zhang, Jiahui Yu, Christian Frank, et al. Noise2music: Text-conditioned music generation with dif- fusion models. arXiv preprint arXiv:2302.03917, 2023.","title":"Noise2music: Text-conditioned music generation with diffusion models","authors":"Huang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Huang et al., 2023b] Rongjie Huang, Jiawei Huang, Dongchao Yang, Yi Ren, Luping Liu, Mingze Li, Zhenhui Ye, Jinglin Liu, Xiang Yin, and Zhou Zhao. Make-an- audio: Text-to-audio generation with prompt-enhanced diffusion models. arXiv preprint arXiv:2301.12661, 2023.","title":"Make-anaudio: Text-to-audio generation with prompt-enhanced diffusion models","authors":"Huang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Jeong et al., 2023] Jaeseok Jeong, Mingi Kwon, and Youngjung Uh. Training-free style transfer emerges from h-space in diffusion models. arXiv preprint arXiv:2303.15403, 2023.","title":"Training-free style transfer emerges from h-space in diffusion models","authors":"Jeong","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Karras et al., 2020] Tero Karras, Samuli Laine, Miika Ait- tala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. An- alyzing and improving the image quality of stylegan. In Proceedings of the IEEE/CVF conference on computer vi- sion and pattern recognition, pages 8110-8119, 2020.","title":"Analyzing and improving the image quality of stylegan","authors":"Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, Timo Aila","year":"2020","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Kilgour et al., 2019] Kevin Kilgour, Mauricio Zuluaga, Do- minik Roblek, and Matthew Sharifi. Fréchet audio dis- tance: A reference-free metric for evaluating music en- hancement algorithms. In INTERSPEECH, pages 2350- 2354, 2019.","title":"Fréchet audio distance: A reference-free metric for evaluating music enhancement algorithms","authors":"Kevin Kilgour, Mauricio Zuluaga, Dominik Roblek, Matthew Sharifi","year":"2019","venue":"INTERSPEECH","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Kwon et al., 2022] Mingi Kwon, Jaeseok Jeong, and Youngjung Uh. Diffusion models already have a semantic latent space. arXiv preprint arXiv:2210.10960, 2022.","title":"Diffusion models already have a semantic latent space","authors":"Kwon","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Lam et al., 2023] Max WY Lam, Qiao Tian, Tang Li, Zongyu Yin, Siyuan Feng, Ming Tu, Yuliang Ji, Rui Xia, Mingbo Ma, Xuchen Song, et al. Efficient neural music generation. arXiv preprint arXiv:2305.15719, 2023.","title":"Efficient neural music generation","authors":"Max Wy Lam, Qiao Tian, Tang Li, Zongyu Yin, Siyuan Feng, Ming Tu, Yuliang Ji, Rui Xia, Mingbo Ma, Xuchen Song","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Liu et al., 2023a] Haohe Liu, Zehua Chen, Yi Yuan, Xin- hao Mei, Xubo Liu, Danilo Mandic, Wenwu Wang, and Mark D Plumbley. Audioldm: Text-to-audio gen- eration with latent diffusion models. arXiv preprint arXiv:2301.12503, 2023.","title":"Audioldm: Text-to-audio generation with latent diffusion models","authors":"Liu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Liu et al., 2023b] Xihui Liu, Dong Huk Park, Samaneh Azadi, Gong Zhang, Arman Chopikyan, Yuxiao Hu, Humphrey Shi, Anna Rohrbach, and Trevor Darrell. More control for free! image synthesis with semantic diffusion guidance. In Proceedings of the IEEE/CVF Winter Confer- ence on Applications of Computer Vision, pages 289-299, 2023.","title":"More control for free! image synthesis with semantic diffusion guidance","authors":"Liu","year":"2023","venue":"Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Lu et al., 2021] Wei-Tsung Lu, Ju-Chiang Wang, Minz Won, Keunwoo Choi, and Xuchen Song. Spectnt: A time- frequency transformer for music audio. arXiv preprint arXiv:2110.09127, 2021.","title":"Spectnt: A timefrequency transformer for music audio","authors":"Wei-Tsung Lu, Ju-Chiang Wang, Minz Won, Keunwoo Choi, Xuchen Song","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Lugmayr et al., 2022] Andreas Lugmayr, Martin Danell- jan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool. Repaint: Inpainting using denoising diffusion probabilistic models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11461-11471, 2022.","title":"Repaint: Inpainting using denoising diffusion probabilistic models","authors":"Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, Luc Van Gool","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Lv et al., 2023] Ang Lv, Xu Tan, Peiling Lu, Wei Ye, Shikun Zhang, Jiang Bian, and Rui Yan. Getmusic: Gen- erating any music tracks with a unified representation and diffusion framework. arXiv preprint arXiv:2305.10841, 2023.","title":"Getmusic: Generating any music tracks with a unified representation and diffusion framework","authors":"Ang Lv, Xu Tan, Peiling Lu, Wei Ye, Shikun Zhang, Jiang Bian, Rui Yan","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Manilow et al., 2019] Ethan Manilow, Gordon Wichern, Prem Seetharaman, and Jonathan Le Roux. Cutting mu- sic source separation some slakh: A dataset to study the impact of training data quality and quantity. In 2019 IEEE WASPAA, pages 45-49. IEEE, 2019.","title":"Cutting music source separation some slakh: A dataset to study the impact of training data quality and quantity","authors":"Ethan Manilow, Gordon Wichern, Prem Seetharaman, Jonathan Le Roux","year":"2019","venue":"IEEE WASPAA","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Meng et al., 2021] Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon. Sdedit: Guided image synthesis and editing with stochastic differential equations. arXiv preprint arXiv:2108.01073, 2021.","title":"Sdedit: Guided image synthesis and editing with stochastic differential equations","authors":"Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, Stefano Ermon","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Nichol et al., 2021] Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. Glide: Towards photore- alistic image generation and editing with text-guided dif- fusion models. arXiv preprint arXiv:2112.10741, 2021.","title":"Glide: Towards photorealistic image generation and editing with text-guided diffusion models","authors":"Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob Mcgrew, Ilya Sutskever, Mark Chen","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Raffel et al., 2020] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research, 21(1):5485- 5551, 2020.","title":"Exploring the limits of transfer learning with a unified text-to-text transformer","authors":"Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J Liu","year":"2020","venue":"The Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Ramesh et al., 2022] Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 2022.","title":"Hierarchical text-conditional image generation with clip latents","authors":"Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Ren et al., 2020] Yi Ren, Jinzheng He, Xu Tan, Tao Qin, Zhou Zhao, and Tie-Yan Liu. Popmag: Pop music accom- paniment generation. In Proceedings of the 28th ACM in- ternational conference on multimedia, pages 1198-1206, 2020.","title":"Popmag: Pop music accompaniment generation","authors":"Yi Ren, Jinzheng He, Xu Tan, Tao Qin, Zhou Zhao, Tie-Yan Liu","year":"2020","venue":"Proceedings of the 28th ACM international conference on multimedia","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Rombach et al., 2022] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj \"orn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022.","title":"High-resolution image synthesis with latent diffusion models","authors":"Rombach","year":"2022","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Ronneberger et al., 2015] Olaf Ronneberger, Philipp Fis- cher, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pages 234- 241. Springer, 2015.","title":"Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation","authors":"Ronneberger","year":"2015","venue":"Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Saharia et al., 2022] Chitwan","title":"Chitwan","authors":"Saharia","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":34,"raw":"Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language un- derstanding. Advances in Neural Information Processing Systems, 35:36479-36494, 2022.","title":"Photorealistic text-to-image diffusion models with deep language understanding","authors":"William Saharia, Saurabh Chan, Lala Saxena, Jay Li, Emily L Whang, Kamyar Denton, Raphael Gontijo Ghasemipour, Burcu Lopes, Tim Karagol Ayan, Salimans","year":"2022","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Schneider et al., 2023] Flavio Schneider, Zhijing Jin, and Bernhard Schölkopf. Mo\\ˆusai: Text-to-music gener- ation with long-context latent diffusion. arXiv preprint arXiv:2301.11757, 2023.","title":"Mo\\ˆusai: Text-to-music generation with long-context latent diffusion","authors":"Flavio Schneider, Zhijing Jin, Bernhard Schölkopf","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Song et al., 2020] Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456, 2020.","title":"Score-based generative modeling through stochastic differential equations","authors":"Song","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Vaswani et al., 2017] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.","title":"Attention is all you need","authors":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, Illia Polosukhin","year":"2017","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Wang et al., 2024] Yuancheng Wang, Zeqian Ju, Xu Tan, Lei He, Zhizheng Wu, Jiang Bian, et al. Audit: Audio edit- ing by following instructions with latent diffusion models. Advances in Neural Information Processing Systems, 36, 2024.","title":"Audit: Audio editing by following instructions with latent diffusion models","authors":"Yuancheng Wang, Zeqian Ju, Xu Tan, Lei He, Zhizheng Wu, Jiang Bian","year":"2024","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Waysdorf, 2021] Abby S Waysdorf. Remix in the age of ubiquitous remix. Convergence, 27(4):1129-1144, 2021.","title":"Remix in the age of ubiquitous remix","authors":"Abby S Waysdorf","year":"2021","venue":"Convergence","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Wierstorf et al., 2017] Hagen Wierstorf, Dominic Ward, Russell Mason, Emad M Grais, Chris Hummersone, and Mark D Plumbley. Perceptual evaluation of source sepa- ration for remixing music. In Audio Engineering Society Convention 143. Audio Engineering Society, 2017.","title":"Perceptual evaluation of source separation for remixing music","authors":"Hagen Wierstorf, Dominic Ward, Russell Mason, M Emad, Chris Grais, Mark D Hummersone, Plumbley","year":"2017","venue":"Audio Engineering Society Convention","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Yang and Lerch, 2020a] Li-Chia Yang and Alexander Lerch. On the evaluation of generative models in music. Neural Computing and Applications, 32(9):4773-4784, 2020.","title":"On the evaluation of generative models in music","authors":"Yang, Lerch ; A] Li-Chia Yang, Alexander Lerch","year":"2020","venue":"Neural Computing and Applications","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Yang and Lerch, 2020b] Li-Chia Yang and Alexander Lerch. Remixing music with visual conditioning. In 2020 IEEE International Symposium on Multimedia (ISM), pages 181-188. IEEE, 2020.","title":"Remixing music with visual conditioning","authors":"Yang, Lerch ; Li-Chia Yang, Alexander Lerch","year":"2020","venue":"IEEE International Symposium on Multimedia (ISM)","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Yang et al., 2022] Haici Yang, Shivani Firodiya, Nicholas J Bryan, and Minje Kim. Don't separate, learn to remix: End-to-end neural remixing with joint optimization. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 116-120. IEEE, 2022.","title":"Don't separate, learn to remix: End-to-end neural remixing with joint optimization","authors":"Yang","year":"2022","venue":"ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Zha et al., 2021] Xuefan Zha, Wentao Zhu, Lv Xun, Sen Yang, and Ji Liu. Shifted chunk transformer for spatio- temporal representational learning. Advances in Neural Information Processing Systems, 34:11384-11396, 2021.","title":"Shifted chunk transformer for spatiotemporal representational learning","authors":"Zha","year":"2021","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}