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We present OpenMU-Bench, a large-scale benchmark suite for addressing the data scarcity issue in training multimodal language models to understand music.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
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Musical genre classification of audio signals
George Tzanetakis and Perry Cook · 2002
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I found it, how can i use it?”-dealing with the ethical and legal constraints of information access
Anthony Seeger · 2003
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An industrial strength audio search algorithm
Avery Wang · 2003
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
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METEOR: An automatic metric for MT evaluation with improved correlation with human judgments
Satanjeev Banerjee and Alon Lavie · 2005
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Content-based music information retrieval: Current directions and future challenges
Michael A. Casey, Remco Veltkamp, Masataka Goto, Marc Leman, Christophe Rhodes, and Malcolm Slaney · 2008
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Roadmap for music information research
Xavier Serra, Michela Magas, Emmanouil Benetos, Magdalena Chudy, Simon Dixon, Arthur Flexer, Emilia Gómez Gutiérrez, Fabien Gouyon, Herrera Boyer, Sergi Jordà Puig, et al · 2013
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Accurate tempo estimation based on recurrent neural networks and resonating comb filters
Sebastian Böck, Florian Krebs, and Gerhard Widmer · 2015
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Deep learning and music adversaries
Corey Kereliuk, Bob L Sturm, and Jan Larsen · 2015
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Madmom: a new Python Audio and Music Signal Processing Library
Sebastian Böck, Filip Korzeniowski, Jan Schlüter, Florian Krebs, and Gerhard Widmer · 2016
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Audio set: An ontology and human-labeled dataset for audio events
Jort F Gemmeke, Daniel PW Ellis, Dylan Freedman, Aren Jansen, Wade Lawrence, R Channing Moore, Manoj Plakal, and Marvin Ritter · 2017
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Bridging nonlinearities and stochastic regularizers with gaussian error linear units, 2017
Dan Hendrycks and Kevin Gimpel · 2017
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Learning features of music from scratch
John Thickstun, Zaid Harchaoui, and Sham Kakade · 2017
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Ethical dimensions of music information retrieval technology
Andre Holzapfel, Bob Sturm, and Mark Coeckelbergh · 2018
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The mtg-jamendo dataset for automatic music tagging
Dmitry Bogdanov, Minz Won, Philip Tovstogan, Alastair Porter, and Xavier Serra · 2019
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An attention mechanism for musical instrument recognition
Siddharth Gururani, Mohit Sharma, and Alexander Lerch · 2019
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Emotion and theme recognition in music with frequency-aware rf-regularized cnns
Khaled Koutini, Shreyan Chowdhury, Verena Haunschmid, Hamid Eghbal-Zadeh, and Gerhard Widmer · 2019
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Evaluation of text generation: A survey
Asli Celikyilmaz, Elizabeth Clark, and Jianfeng Gao · 2020
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Jukebox: A generative model for music
Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, and Ilya Sutskever · 2020
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Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann · 2020
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Music4all: A new music database and its applications
Igor André Pegoraro Santana, Fabio Pinhelli, Juliano Donini, Leonardo Catharin, Rafael Biazus Mangolin, Yandre Maldonado e Gomes da Costa, Valéria Delisandra Feltrim, and Marcos Aurélio Domingues · 2020
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Zero: Memory optimizations toward training trillion parameter models
Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He · 2020
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Lyrics information processing: Analysis, generation, and applications
Kento Watanabe and Masataka Goto · 2020
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Evaluation of cnn-based automatic music tagging models
Minz Won, Andres Ferraro, Dmitry Bogdanov, and Xavier Serra · 2020
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Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi · 2020
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Codified audio language modeling learns useful representations for music information retrieval
Rodrigo Castellon, Chris Donahue, and Percy Liang · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
Mme: A comprehensive evaluation benchmark for multimodal large language models
Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Jinrui Yang, Xiawu Zheng, Ke Li, Xing Sun, et al · 2023
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M2ugen: Multi-modal music understanding and generation with the power of large language models
Atin Sakkeer Hussain, Shansong Liu, Chenshuo Sun, and Ying Shan · 2023
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Introducing mpt-7b: A new standard for open-source, commercially usable llms, 2023
MosaicML-NLP-Team · 2023
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Tool learning with foundation models
Yujia Qin, Shengding Hu, Yankai Lin, Weize Chen, Ning Ding, Ganqu Cui, Zheni Zeng, Yufei Huang, Chaojun Xiao, Chi Han, Yi Ren Fung, Yusheng Su, Huadong Wang, Cheng Qian, Runchu Tian, Kunlun Zhu, Shi Liang, Xingyu Shen, Bokai Xu, Zhen Zhang, Yining Ye, Bo Li, Ziwei Tang, Jing Yi, Yu Zhu, Zhenning Dai, Lan Yan, Xin Cong, Ya-Ting Lu, Weilin Zhao, Yuxiang Huang, Jun-Han Yan, Xu Han, Xian Sun, Dahai Li, Jason Phang, Cheng Yang, Tongshuang Wu, Heng Ji, Zhiyuan Liu, and Maosong Sun · 2023
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Mt3: Multi-task multitrack music transcription
Josh Gardner, Ian Simon, Ethan Manilow, Curtis Hawthorne, and Jesse Engel · 2021
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2021
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Efficient training of audio transformers with patchout
Khaled Koutini, Jan Schlüter, Hamid Eghbal-Zadeh, and Gerhard Widmer · 2021
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Muscaps: Generating captions for music audio
Ilaria Manco, Emmanouil Benetos, Elio Quinton, and György Fazekas · 2021
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Musical tempo estimation using a multi-scale network
Xiaoheng Sun, Qiqi He, Yongwei Gao, and Wei Li · 2021
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Multimodal few-shot learning with frozen language models
Maria Tsimpoukelli, Jacob L Menick, Serkan Cabi, SM Eslami, Oriol Vinyals, and Felix Hill · 2021
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Flashattention: Fast and memory-efficient exact attention with io-awareness
Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, and Christopher Ré · 2022
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Automatic piano transcription with hierarchical frequency-time transformer
Keisuke Toyama, Taketo Akama, Yukara Ikemiya, Yuhta Takida, Wei-Hsiang Liao, and Yuki Mitsufuji · 2023
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mplug-owl: Modularization empowers large language models with multimodality
Qinghao Ye, Haiyang Xu, Guohai Xu, Jiabo Ye, Ming Yan, Yiyang Zhou, Junyang Wang, Anwen Hu, Pengcheng Shi, Yaya Shi, et al · 2023
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A survey on multimodal large language models
Shukang Yin, Chaoyou Fu, Sirui Zhao, Ke Li, Xing Sun, Tong Xu, and Enhong Chen · 2023
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Marble: Music audio representation benchmark for universal evaluation
Ruibin Yuan, Yinghao Ma, Yizhi Li, Ge Zhang, Xingran Chen, Hanzhi Yin, Le Zhuo, Yiqi Liu, Jiawen Huang, Zeyue Tian, et al · 2023
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Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi. arxiv, 2023
X Yue, Y Ni, K Zhang, T Zheng, R Liu, G Zhang, S Stevens, D Jiang, W Ren, Y Sun, et al · 2023
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Video-LLaMA: An instruction-tuned audio-visual language model for video understanding
Hang Zhang, Xin Li, and Lidong Bing · 2023
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An attention-based approach to hierarchical multi-label music instrument classification
Zhi Zhong, Masato Hirano, Kazuki Shimada, Kazuya Tateishi, Shusuke Takahashi, and Yuki Mitsufuji · 2023
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Minigpt-4: Enhancing vision-language understanding with advanced large language models
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny · 2023
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MusiLingo: Bridging music and text with pre-trained language models for music captioning and query response
Zihao Deng, Yinghao Ma, Yudong Liu, Rongchen Guo, Ge Zhang, Wenhu Chen, Wenhao Huang, and Emmanouil Benetos · 2024
Closest in time.
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al · 2024
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Llark: A multimodal instruction-following language model for music
Josh Gardner, Simon Durand, Daniel Stoller, and Rachel Bittner · 2024
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Listen, think, and understand
Yuan Gong, Hongyin Luo, Alexander H. Liu, Leonid Karlinsky, and James R. Glass · 2024
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MERT: Acoustic music understanding model with large-scale self-supervised training
Yizhi Li, Ruibin Yuan, Ge Zhang, Yinghao Ma, Xingran Chen, Hanzhi Yin, Chenghao Xiao, Chenghua Lin, Anton Ragni, Emmanouil Benetos, Norbert Gyenge, Roger Dannenberg, Ruibo Liu, Wenhu Chen, Gus Xia, Yemin Shi, Wenhao Huang, Zili Wang, Yike Guo, and Jie Fu · 2024
Closest in time.
Music understanding llama: Advancing text-to-music generation with question answering and captioning
Shansong Liu, Atin Sakkeer Hussain, Chenshuo Sun, and Ying Shan · 2024
Closest in time.
Mm1: Methods, analysis & insights from multimodal llm pre-training
Brandon McKinzie, Zhe Gan, Jean-Philippe Fauconnier, Sam Dodge, Bowen Zhang, Philipp Dufter, Dhruti Shah, Xianzhi Du, Futang Peng, Floris Weers, et al · 2024
Closest in time.
Muchomusic: Evaluating music understanding in multimodal audio-language models
Benno Weck, Ilaria Manco, Emmanouil Benetos, Elio Quinton, György Fazekas, and Dmitry Bogdanov · 2024
Closest in time.
A foundation model for music informatics
Minz Won, Yun-Ning Hung, and Duc Le · 2024
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