@inproceedings{deng-etal-2024-musilingo,
title = "{M}usi{L}ingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response",
author = "Deng, Zihao and
Ma, Yinghao and
Liu, Yudong and
Guo, Rongchen and
Zhang, Ge and
Chen, Wenhu and
Huang, Wenhao and
Benetos, Emmanouil",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-naacl.231",
pages = "3643--3655",
abstract = "Large Language Models (LLMs) have shown immense potential in multimodal applications, yet the convergence of textual and musical domains remains not well-explored. To address this gap, we present MusiLingo, a novel system for music caption generation and music-related query responses. MusiLingo employs a single projection layer to align music representations from the pre-trained frozen music audio model MERT (CITATION) with a frozen LLM, bridging the gap between music audio and textual contexts. We train it on an extensive music caption dataset and fine-tune it with instructional data. Due to the scarcity of high-quality music Q{\&}A datasets, we created the MusicInstruct (MI) dataset from captions in the MusicCaps datasets, tailored for open-ended music inquiries. Empirical evaluations demonstrate its competitive performance in generating music captions and composing music-related Q{\&}A pairs. Our introduced dataset enables notable advancements beyond previous ones.",
}
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<abstract>Large Language Models (LLMs) have shown immense potential in multimodal applications, yet the convergence of textual and musical domains remains not well-explored. To address this gap, we present MusiLingo, a novel system for music caption generation and music-related query responses. MusiLingo employs a single projection layer to align music representations from the pre-trained frozen music audio model MERT (CITATION) with a frozen LLM, bridging the gap between music audio and textual contexts. We train it on an extensive music caption dataset and fine-tune it with instructional data. Due to the scarcity of high-quality music Q&A datasets, we created the MusicInstruct (MI) dataset from captions in the MusicCaps datasets, tailored for open-ended music inquiries. Empirical evaluations demonstrate its competitive performance in generating music captions and composing music-related Q&A pairs. Our introduced dataset enables notable advancements beyond previous ones.</abstract>
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%0 Conference Proceedings
%T MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response
%A Deng, Zihao
%A Ma, Yinghao
%A Liu, Yudong
%A Guo, Rongchen
%A Zhang, Ge
%A Chen, Wenhu
%A Huang, Wenhao
%A Benetos, Emmanouil
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F deng-etal-2024-musilingo
%X Large Language Models (LLMs) have shown immense potential in multimodal applications, yet the convergence of textual and musical domains remains not well-explored. To address this gap, we present MusiLingo, a novel system for music caption generation and music-related query responses. MusiLingo employs a single projection layer to align music representations from the pre-trained frozen music audio model MERT (CITATION) with a frozen LLM, bridging the gap between music audio and textual contexts. We train it on an extensive music caption dataset and fine-tune it with instructional data. Due to the scarcity of high-quality music Q&A datasets, we created the MusicInstruct (MI) dataset from captions in the MusicCaps datasets, tailored for open-ended music inquiries. Empirical evaluations demonstrate its competitive performance in generating music captions and composing music-related Q&A pairs. Our introduced dataset enables notable advancements beyond previous ones.
%U https://aclanthology.org/2024.findings-naacl.231
%P 3643-3655
Markdown (Informal)
[MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response](https://aclanthology.org/2024.findings-naacl.231) (Deng et al., Findings 2024)
ACL
- Zihao Deng, Yinghao Ma, Yudong Liu, Rongchen Guo, Ge Zhang, Wenhu Chen, Wenhao Huang, and Emmanouil Benetos. 2024. MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 3643–3655, Mexico City, Mexico. Association for Computational Linguistics.