Futga: Towards Fine-grained Music Understanding through Temporally-enhanced Generative Augmentation (2407.20445v1)

Abstract: Existing music captioning methods are limited to generating concise global descriptions of short music clips, which fail to capture fine-grained musical characteristics and time-aware musical changes. To address these limitations, we propose FUTGA, a model equipped with fined-grained music understanding capabilities through learning from generative augmentation with temporal compositions. We leverage existing music caption datasets and LLMs to synthesize fine-grained music captions with structural descriptions and time boundaries for full-length songs. Augmented by the proposed synthetic dataset, FUTGA is enabled to identify the music's temporal changes at key transition points and their musical functions, as well as generate detailed descriptions for each music segment. We further introduce a full-length music caption dataset generated by FUTGA, as the augmentation of the MusicCaps and the Song Describer datasets. We evaluate the automatically generated captions on several downstream tasks, including music generation and retrieval. The experiments demonstrate the quality of the generated captions and the better performance in various downstream tasks achieved by the proposed music captioning approach. Our code and datasets can be found in \href{https://huggingface.co/JoshuaW1997/FUTGA}{\textcolor{blue}{https://huggingface.co/JoshuaW1997/FUTGA}}.

Towards Fine-grained Music Understanding through Temporally-enhanced Generative Augmentation

The paper "Futga: Towards Fine-grained Music Understanding through Temporally-enhanced Generative Augmentation" addresses the limitations of existing music captioning methods. Typical models in the landscape of music information retrieval (MIR) are primarily focused on generating global descriptions for short music clips, which fails to capture the intricate musical characteristics and temporal shifts present in full-length songs. This paper introduces a novel model, FUTGA, which is designed to provide fine-grained, time-aware music captions.

Methodology

The authors propose FUTGA, a generative music understanding model that leverages temporally-structured data synthesis to annotate long-form musical pieces. The model augmentation uses existing datasets such as MusicCaps and Song Describer, along with LLMs, to generate synthetic datasets comprising fine-grained captions. These captions include detailed structural descriptions and temporal boundaries, which help in identifying key musical changes and transition points.

FUTGA employs a two-pronged approach for dataset construction and model training:

1. Synthetic Music Caption Augmentation: By composing multiple short music clips into synthetic full-length songs and generating corresponding temporal captions, FUTGA captures the dynamic nature of music over time. The authors use importance sampling based on semantic embeddings to ensure coherence in synthetic music, enhancing the realism of the augmented data.
2. Temporally-enhanced Music Understanding: Using a text-only LLM, the model paraphrases and augments template-based captions by incorporating additional data such as global descriptions, musical transitions, and structural tags. These enhanced captions offer a comprehensive view of an entire song's structure and progression.

Experimental Results

The experiments demonstrate FUTGA's superior ability to generate detailed music captions and its improved performance across multiple downstream tasks. Specifically:

• Caption Generation: FUTGA outperforms existing models in terms of providing detailed, segment-specific descriptions for long-form music, with metrics indicating significant enhancements in BLEU, METEOR, ROUGE, and BERT-score evaluations.
• Music Retrieval: The model's many-to-many retrieval method, enabled by time-segmented descriptions, shows improved retrieval performance, particularly on the Song Describer dataset where FUTGA surpasses human annotation baselines.
• Music Generation: FUTGA's detailed captions also enhance text-to-music generation tasks. When finetuned on FUTGA-augmented datasets, models such as MusicLDM show improved alignment with the provided musical descriptions.

Implications and Future Directions

The proposed method has significant implications for the field of MIR and extends the potential use cases for music understanding models. By incorporating temporal and structural annotations, FUTGA enables a more nuanced comprehension of musical compositions. This development opens pathways for more sophisticated applications in music generation, editing, and retrieval.

Future advancements could focus on developing long-context-based CLAP models, which would further enhance the ability to retrieve and interact with comprehensive music datasets. Additionally, extending this approach to other complex music understanding tasks, such as music question-answering and comprehensive song generation, could be highly fruitful.

In conclusion, FUTGA represents a meaningful step towards fine-grained, temporally-aware music comprehension. By leveraging synthetic data augmentation and LLMs, this work enriches the MIR community's toolkit, enabling deeper insights into musical structures and transitions. This approach not only improves existing methodologies but also sets the stage for future innovations in the domain of AI-driven music understanding.