LP-MusicCaps: LLM-Based Pseudo Music Captioning (2307.16372v1)

Abstract: Automatic music captioning, which generates natural language descriptions for given music tracks, holds significant potential for enhancing the understanding and organization of large volumes of musical data. Despite its importance, researchers face challenges due to the costly and time-consuming collection process of existing music-language datasets, which are limited in size. To address this data scarcity issue, we propose the use of LLMs to artificially generate the description sentences from large-scale tag datasets. This results in approximately 2.2M captions paired with 0.5M audio clips. We term it LLM based Pseudo music caption dataset, shortly, LP-MusicCaps. We conduct a systemic evaluation of the large-scale music captioning dataset with various quantitative evaluation metrics used in the field of natural language processing as well as human evaluation. In addition, we trained a transformer-based music captioning model with the dataset and evaluated it under zero-shot and transfer-learning settings. The results demonstrate that our proposed approach outperforms the supervised baseline model.

LP-MusicCaps: LLM-Based Pseudo Music Captioning

The research paper titled "LP-MusicCaps: LLM-Based Pseudo Music Captioning" addresses the challenges faced in the domain of automatic music captioning, which involves generating descriptive language for music tracks. The primary focus is on overcoming the data scarcity issue due to the limited size and costly nature of existing music-language datasets. The authors present a novel approach to this problem by leveraging LLMs to create a pseudo-music captioning dataset.

Approach and Methodology

To generate captions, the authors employ LLMs to transform large-scale tag datasets into descriptive sentences. This results in a substantial dataset comprising approximately 2.2 million captions paired with 0.5 million audio clips, termed LP-MusicCaps. The captioning process is guided by carefully crafted task instructions designed to produce semantically consistent, grammatically correct, and diverse captions. The LLM-based process utilizes various task instructions, including "Writing," "Summary," "Paraphrase," and "Attribute Prediction," which cater to different aspects of music description, enhancing the dataset's quality and variety.

Evaluation and Results

The paper provides a systemic evaluation of the LP-MusicCaps dataset using a combination of quantitative and human evaluations. The quantitative assessment includes traditional NLP metrics like BLEU, ROUGE, and METEOR, as well as neural metrics such as BERT-Score. These evaluations demonstrate the effectiveness of the proposed method, with captions generated using LP-MusicCaps outperforming existing supervised baseline models. Human evaluations further confirm the quality of the generated captions, highlighting their relevance and accuracy compared to ground truth data.

Implications and Applications

The research has significant implications for the field of music information retrieval, offering a cost-effective solution to the data scarcity problem that hinders the development of robust music captioning models. By providing a large-scale pseudo caption dataset, LP-MusicCaps facilitates further research and development in music-to-LLMs, potentially benefiting applications in music recommendation, organization, and understanding.

Future Directions

The success of LP-MusicCaps suggests several avenues for future research. One potential direction is exploring the integration of LLM-generated captions into existing MIR systems to enhance their performance. Additionally, further refinement of task instructions could improve the semantic accuracy of captions. As LLMs continue to advance, leveraging their capabilities for other aspects of audio understanding remains a promising area of exploration.

Conclusion

The paper presents a practical and innovative solution to the challenges of data scarcity in music captioning by using LLMs to generate high-quality pseudo captions. The resulting LP-MusicCaps dataset offers a valuable resource for advancing research in music information retrieval and related fields. Through careful evaluation and validation, the research underscores the potential of LLM-based pseudo music captioning to contribute significantly to the understanding and organization of musical data.