Cross-Attention with Confidence Weighting for Multi-Channel Audio Alignment

2025-09-21

AI Summary

Multi-channel audio alignment faces two key challenges: difficulty in modeling nonlinear clock drift and the absence of uncertainty quantification. To address these, we propose a novel framework integrating cross-attention mechanisms with confidence-weighted scoring. Specifically, we extend the BEATs encoder with dedicated cross-attention layers to explicitly capture inter-channel temporal dependencies. Additionally, we introduce a confidence-weighted scoring mechanism grounded in predictive distributions, enabling probabilistic alignment outputs that overcome the limitations of conventional point estimates. To our knowledge, this is the first approach to achieve interpretable uncertainty estimation and robust synchronization in multi-channel bioacoustic alignment. Evaluated on the BioDCASE 2025 Task 1 benchmark, our method achieves state-of-the-art performance, ranking first with a test-set average MSE of 0.30 (0.14 for ARU recordings and 0.45 for zebra finch vocalizations), substantially outperforming existing baselines.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

Multi-channel audio alignment is a key requirement in bioacoustic monitoring, spatial audio systems, and acoustic localization. However, existing methods often struggle to address nonlinear clock drift and lack mechanisms for quantifying uncertainty. Traditional methods like Cross-correlation and Dynamic Time Warping assume simple drift patterns and provide no reliability measures. Meanwhile, recent deep learning models typically treat alignment as a binary classification task, overlooking inter-channel dependencies and uncertainty estimation. We introduce a method that combines cross-attention mechanisms with confidence-weighted scoring to improve multi-channel audio synchronization. We extend BEATs encoders with cross-attention layers to model temporal relationships between channels. We also develop a confidence-weighted scoring function that uses the full prediction distribution instead of binary thresholding. Our method achieved first place in the BioDCASE 2025 Task 1 challenge with 0.30 MSE average across test datasets, compared to 0.58 for the deep learning baseline. On individual datasets, we achieved 0.14 MSE on ARU data (77% reduction) and 0.45 MSE on zebra finch data (18% reduction). The framework supports probabilistic temporal alignment, moving beyond point estimates. While validated in a bioacoustic context, the approach is applicable to a broader range of multi-channel audio tasks where alignment confidence is critical. Code available on: https://github.com/Ragib-Amin-Nihal/BEATsCA

Problem

• Addressing nonlinear clock drift in multi-channel audio alignment systems
• Providing uncertainty quantification mechanisms for temporal alignment reliability
• Modeling inter-channel dependencies beyond binary classification approaches

Innovation

• Combines cross-attention mechanisms with confidence-weighted scoring
• Extends BEATs encoders with cross-attention for temporal relationships
• Uses full prediction distribution instead of binary thresholding