Robust Audio-Text Retrieval via Cross-Modal Attention and Hybrid Loss

• URL: http://arxiv.org/abs/2604.23323v1
• Date: Sat, 25 Apr 2026 14:17:56 GMT
• Title: Robust Audio-Text Retrieval via Cross-Modal Attention and Hybrid Loss
• Abstract summary: We propose a novel multimodal retrieval framework that refines audio and text embeddings using a cross-modal embedding refinement module.<n>Our approach efficiently handles long-form and noisy audio (SNR 5 to 15) via silence-aware chunking and attention-based pooling.
• Score: 37.71541019854872
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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