ACAVCaps: Enabling large-scale training for fine-grained and diverse audio understanding
Abstract
General audio understanding is a fundamental goal for large audio-language models, with audio captioning serving as a cornerstone task for their development. However, progress in this domain is hindered by existing datasets, which lack the scale and descriptive granularity required to train truly versatile models. To address this gap, we introduce ACAVCaps, a new large-scale, fine-grained, and multi-faceted audio captioning dataset. Derived from the ACAV100M collection, ACAVCaps is constructed using a multi-expert pipeline that analyzes audio from diverse perspectives-including speech, music, and acoustic properties-which are then synthesized into rich, detailed descriptions by a large language model. Experimental results demonstrate that models pre-trained on ACAVCaps exhibit substantially stronger generalization capabilities on various downstream tasks compared to those trained on other leading captioning datasets. The dataset is available at https://github.com/xiaomi-research/acavcaps.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of ACAVCaps, a novel large-scale audio captioning dataset that significantly enhances the granularity and diversity of audio understanding, thereby advancing the development of robust audio-language models. The methodology and experimental validation presented in this work position it as a valuable resource for future research in the field of audio processing and multimodal learning.
Methodology
The methodology for constructing the ACAVCaps dataset is innovative, utilizing a multi-expert pipeline that integrates various analytical perspectives (speech, music, acoustic properties) and synthesizes detailed descriptions using a large language model (LLM). This approach addresses the limitations of existing datasets by ensuring both scale and descriptive granularity, which are crucial for training versatile audio models. The use of Chain-of-Thought (CoT) prompting for LLMs to generate diverse and semantically rich captions is particularly noteworthy, as it enhances the quality of the generated annotations.
Results & evaluation
The experimental evaluation is robust, demonstrating clear superiority of models trained on ACAVCaps across various downstream tasks compared to other datasets. The use of comprehensive benchmarks like MECAT-Caption and the detailed analysis of generalization performance across multiple audio domains (speech, sound events, music) provide strong evidence of the dataset's effectiveness. The results are quantitatively supported by metrics that emphasize both descriptive specificity and semantic similarity, reinforcing the dataset's intended impact.
Reproducibility
The paper provides sufficient implementation details regarding the training process, model architecture, and evaluation metrics. However, the reproducibility may be limited by the lack of access to the specific expert models used in the multi-expert pipeline, which are crucial for generating the dataset. The dataset itself is available, which aids in reproducibility, but the exact configurations and parameters for the LLM and expert models could be better documented.
Limitations
One limitation is the potential bias introduced by the expert models used for audio analysis, which may not capture all nuances of audio content. Additionally, while the dataset is large and diverse, it may still miss certain rare or unique audio events that could be important for comprehensive audio understanding. The reliance on automated processes for generating captions might also lead to inconsistencies in quality across different audio samples.
Broader impact
The introduction of ACAVCaps has significant implications for the field of audio understanding and multimodal AI. By providing a rich, large-scale dataset, it enables the development of more capable audio-language models that can generalize better across various tasks. This can lead to advancements in applications such as automatic audio transcription, sound event detection, and even creative audio generation, ultimately enhancing the capabilities of AI systems in understanding and interacting with the auditory world. The main contribution of this paper is the introduction of ACAVCaps, a novel large-scale audio captioning dataset that significantly enhances the granularity and diversity of audio understanding, thereby advancing the development of robust audio-language models. The methodology and experimental validation presented in this work position it as a valuable resource for future research in the field of audio processing and multimodal learning.
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