Data-Efficient Playlist Captioning With Musical and Linguistic Knowledge (2022.emnlp-main)
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| Challenge: | Music streaming services feature billions of playlists created by users, professional editors or algorithms. |
| Approach: | They propose a multi-modal encoder-decoder model for automatic playlist captioning that leverages linguistic and musical knowledge to generate correct and thematic captions. |
| Outcome: | The proposed model yields 2x-3x higher BLEU@4 and CIDEr than state-of-the-art captioning algorithms on a new playlists dataset from two major streaming services. |
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| Challenge: | Traditional approaches to music captioning ignore the intricate interplay between the two . however, a comprehensive understanding of music necessitates the integration of both these elements. |
| Approach: | They propose a method to learn multimodal alignment between audio and lyrics through contrastive learning. |
| Outcome: | The proposed method achieves new state-of-the-art on two music captioning datasets. |
MusicAgent: An AI Agent for Music Understanding and Generation with Large Language Models (2023.emnlp-demo)
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| Challenge: | MusicAgent integrates numerous music-related tools and an autonomous workflow to address user requirements. |
| Approach: | a new system is built to integrate music-related tools and an autonomous workflow . the system is based on large language models (LLMs) that can be used to organize and decompose requests . |
| Outcome: | the proposed system integrates numerous music-related tools and an autonomous workflow to address user requirements. |
AudioCaps: Generating Captions for Audios in The Wild (N19-1)
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| Challenge: | a dataset of 46K audio clips with human-written text pairs is used to generate captions for audio . the task of translating a multimedia input source into natural language has been extensively studied over the past few years . |
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MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response (2024.findings-naacl)
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Zihao Deng, Yinghao Ma, Yudong Liu, Rongchen Guo, Ge Zhang, Wenhu Chen, Wenhao Huang, Emmanouil Benetos
| Challenge: | Large Language Models have shown immense potential in multimodal applications, but convergence between textual and musical domains remains unexplored. |
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| Outcome: | The proposed system bridges the gap between music audio and textual contexts by combining music captions with a frozen model . it performs well in generating music caption and composing music-related Q&A pairs . the proposed system is available for free download at http://www.musilingo.com/ . |
Translate the Beauty in Songs: Jointly Learning to Align Melody and Translate Lyrics (2023.findings-emnlp)
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| Challenge: | Song translation requires both translation of lyrics and alignment of music notes . human translators of songs need to have a mastery of cultural traditions and the poetic usage of both source and target languages . |
| Approach: | They propose a model that can model lyric translation and lyrics-melody alignment . they use an encoder-decoder framework that can translate lyrics and determine number of aligned notes . |
| Outcome: | The proposed framework can translate lyrics and determine the number of aligned notes at each decoding step. |
Mustango: Toward Controllable Text-to-Music Generation (2024.naacl-long)
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| Challenge: | Mustango is a text-to-music system that allows music-domain-knowledge-informed text-based music generation. |
| Approach: | They propose a music-domain-knowledge-inspired text-to-music system based on diffusion that generates music with captions that include specific instructions related to chords, beats, key and tempo. |
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FIGMA: Towards FIne-Grained Music retrievAl (2026.acl-long)
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| Challenge: | Existing music retrieval models fail to retrieve fine-grained musical attributes when using coarse semantic queries. |
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VisText: A Benchmark for Semantically Rich Chart Captioning (2023.acl-long)
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| Challenge: | Current approaches for automatically generating chart captions struggle to articulate the perceptual or cognitive features that are the hallmark of charts (e.g., complex trends and patterns). |
| Approach: | They propose a dataset of 12,441 pairs of charts and captions that describe charts’ construction, report key statistics, and identify perceptual and cognitive phenomena. |
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Generative Music Models’ Alignment with Professional and Amateur Users’ Expectations (2025.findings-acl)
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| Challenge: | Recent years have witnessed rapid advances in text-to-music generation using large language models. |
| Approach: | They propose a task to align AI-generated music with human expressions . they use a dataset of over 1.5 million songs to analyze their content . |
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Chart-to-Text: A Large-Scale Benchmark for Chart Summarization (2022.acl-long)
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Shankar Kantharaj, Rixie Tiffany Leong, Xiang Lin, Ahmed Masry, Megh Thakkar, Enamul Hoque, Shafiq Joty
| Challenge: | Inferring key insights from charts can be challenging and time-consuming. |
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