Robust Audio-Text Retrieval via Cross-Modal Attention and Hybrid Loss
Abstract
Audio-text retrieval enables semantic alignment between audio content and natural language queries, supporting applications in multimedia search, accessibility, and surveillance. However, current state-of-the-art approaches struggle with long, noisy, and weakly labeled audio due to their reliance on contrastive learning and large-batch training. We propose a novel multimodal retrieval framework that refines audio and text embeddings using a cross-modal embedding refinement module combining transformer-based projection, linear mapping, and bidirectional attention. To further improve robustness, we introduce a hybrid loss function blending cosine similarity, $\mathcal{L}_{1}$, and contrastive objectives, enabling stable training even under small-batch constraints. Our approach efficiently handles long-form and noisy audio (SNR 5 to 15) via silence-aware chunking and attention-based pooling. Experiments on benchmark datasets demonstrate improvements over prior methods.
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 a novel multimodal retrieval framework that leverages cross-modal attention and a hybrid loss function to improve audio-text retrieval performance, particularly in challenging conditions involving long and noisy audio. This work represents a meaningful advancement in the field, addressing critical limitations of existing methods and providing a foundation for future research in audio-text alignment and retrieval systems.
Methodology
The proposed methodology introduces a robust framework for audio-text retrieval that effectively addresses the limitations of existing models by incorporating a cross-modal embedding refinement module and a hybrid loss function. The use of transformer-based projections and attention mechanisms allows for deep semantic alignment between audio and text, which is particularly beneficial for handling long and noisy audio inputs. The silence-aware chunking and attention-based pooling strategies are innovative approaches that enhance the model's robustness against irrelevant or corrupted segments. Overall, the methodology is well-structured and demonstrates a clear advancement over previous techniques.
Results & evaluation
The experimental evaluation is thorough, utilizing multiple benchmark datasets (FSD50K, ESC-50, Clotho, and AudioCaps) to validate the proposed approach. The results show significant improvements in retrieval performance compared to existing models, with statistical significance established through paired Wilcoxon tests. The ablation studies provide insights into the contributions of various components of the model, reinforcing the importance of the proposed hybrid loss function and transformer-based projection layers. However, the paper could benefit from additional qualitative analysis of retrieval examples to further illustrate the model's strengths and weaknesses.
Reproducibility
The paper provides adequate implementation details, including the choice of encoders and the training process. However, the absence of a public code repository or demo URL limits reproducibility. Future work should consider releasing the code and trained models to facilitate further research and validation by the community.
Limitations
The paper acknowledges several limitations, including dependence on high-quality pretrained encoders, potential performance degradation in extremely noisy environments, and the coarse nature of silence-based segmentation. These limitations highlight areas for future research, such as exploring adaptive segmentation techniques and evaluating the model's performance in more complex acoustic environments.
Broader impact
The framework has significant potential applications in multimedia search, accessibility, and surveillance, making it relevant to various fields. However, ethical considerations regarding privacy, bias, and misuse risks must be addressed to ensure responsible deployment. The model's robustness to noise and ability to handle multi-event audio could enhance accessibility tools for individuals with hearing impairments. The main contribution of this paper is a novel multimodal retrieval framework that leverages cross-modal attention and a hybrid loss function to improve audio-text retrieval performance, particularly in challenging conditions involving long and noisy audio. This work represents a meaningful advancement in the field, addressing critical limitations of existing methods and providing a foundation for future research in audio-text alignment and retrieval systems.
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