FineLAP: Taming Heterogeneous Supervision for Fine-grained Language-Audio Pretraining
Abstract
Contrastively pretrained audio-language models (e.g., CLAP) excel at clip-level understanding but struggle with frame-level tasks. Existing extensions fail to exploit the varying granularity of real-world audio-text data, where massive clip-level textual descriptions coexist with limited frame-level annotations. This paper proposes Fine-grained Language-Audio Pretraining (FineLAP), a novel training paradigm that advances both clip- and frame-level alignment in CLAP with heterogeneous data. FineLAP introduces a dual-stream sigmoid loss with a cluster-based sampling strategy to jointly learn from clip- and frame-level supervision. To capture both global semantics and local details, FineLAP uses a decoupled audio projector on top of a self-supervised encoder. To alleviate the scarcity of temporally annotated data, we present FineLAP-100k, a large-scale synthetic SED dataset constructed through a scalable curation pipeline. Extensive experiments demonstrate that FineLAP achieves SOTA performance across multiple audio understanding tasks, including retrieval, classification, sound event detection, and text-to-audio grounding. Ablation studies further show that coarse- and fine-grained alignment are mutually beneficial, providing insights for building better audio-language models (ALMs).
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
FineLAP presents a novel training paradigm that effectively combines heterogeneous supervision for fine-grained audio-language pretraining. The comprehensive methodology and robust experimental validation position it as a significant contribution to the field of audio understanding, with potential applications across diverse domains.
Methodology
The methodology presented in FineLAP is innovative, addressing the challenge of heterogeneous supervision in audio-language models. The introduction of a dual-stream sigmoid loss and a decoupled audio projector allows for effective learning from both clip- and frame-level annotations. This approach is well-justified, as it leverages the strengths of existing models while introducing novel components that enhance performance across various tasks. The use of cluster-based sampling for negative phrases is particularly noteworthy, as it mitigates the scarcity of frame-level annotations and improves the model's ability to generalize.
Results & evaluation
The experiments conducted are extensive and demonstrate the effectiveness of FineLAP across multiple audio understanding tasks, achieving state-of-the-art results. The evaluation includes a variety of benchmarks, and the ablation studies provide clear insights into the contributions of each component of the model. The results are compelling, showing significant improvements over existing methods, particularly in sound event detection and audio-text retrieval.
Reproducibility
The paper provides sufficient implementation details, including training parameters and dataset descriptions, which are crucial for reproducibility. The authors also commit to releasing the code and dataset, which enhances the potential for other researchers to replicate and build upon their work.
Limitations
Despite its strengths, FineLAP has limitations, such as its inability to handle variable-length audio inputs, which restricts its applicability in scenarios requiring long-form audio processing. Additionally, the focus on sound event detection may overlook other temporally grounded tasks, indicating areas for future exploration.
Broader impact
The advancements made in FineLAP have significant implications for audio understanding and multimodal learning, particularly in applications such as automated audio captioning, sound event detection, and audio editing. The model's ability to leverage heterogeneous data could lead to more robust and flexible audio-language systems, potentially benefiting various industries, including entertainment, accessibility, and security. FineLAP presents a novel training paradigm that effectively combines heterogeneous supervision for fine-grained audio-language pretraining. The comprehensive methodology and robust experimental validation position it as a significant contribution to the field of audio understanding, with potential applications across diverse domains.
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