音频时空融合的自适应证据加权

摘要

许多机器学习系统能够获取同一预测目标的多种证据来源，但这些来源在不同输入中的可靠性和信息量往往存在差异。在生物声学分类中，物种身份既可从声学信号推断，也可通过时空上下文（如地理位置和季节）来判定；虽然贝叶斯推断支持采用乘法证据融合策略，但实践中我们通常只能使用判别式预测器而非经过校准的生成模型。我们提出独立条件假设融合框架（FINCH），这是一种自适应对数线性证据融合方法，将预训练的音频分类器与结构化时空预测器相集成。FINCH通过逐样本门控函数，根据不确定性和信息量统计量估计上下文信息的可靠性。该融合框架以纯音频分类器为特例，显式约束上下文证据的影响范围，形成具有可解释纯音频回退机制的风险可控假设类。在多个基准测试中，FINCH始终优于固定权重融合和纯音频基线模型，即使上下文信息本身较弱时也能提升鲁棒性并优化误差权衡。通过轻量化、可解释的证据融合方法，我们在CBI数据集上实现了最先进性能，并在BirdSet的多个子集上取得竞争性或更优的结果。代码已开源：\href{https://anonymous.4open.science/r/birdnoise-85CD/README.md}{匿名仓库}

English

Many machine learning systems have access to multiple sources of evidence for the same prediction target, yet these sources often differ in reliability and informativeness across inputs. In bioacoustic classification, species identity may be inferred both from the acoustic signal and from spatiotemporal context such as location and season; while Bayesian inference motivates multiplicative evidence combination, in practice we typically only have access to discriminative predictors rather than calibrated generative models. We introduce Fusion under INdependent Conditional Hypotheses (FINCH), an adaptive log-linear evidence fusion framework that integrates a pre-trained audio classifier with a structured spatiotemporal predictor. FINCH learns a per-sample gating function that estimates the reliability of contextual information from uncertainty and informativeness statistics. The resulting fusion family contains the audio-only classifier as a special case and explicitly bounds the influence of contextual evidence, yielding a risk-contained hypothesis class with an interpretable audio-only fallback. Across benchmarks, FINCH consistently outperforms fixed-weight fusion and audio-only baselines, improving robustness and error trade-offs even when contextual information is weak in isolation. We achieve state-of-the-art performance on CBI and competitive or improved performance on several subsets of BirdSet using a lightweight, interpretable, evidence-based approach. Code is available: \href{https://anonymous.4open.science/r/birdnoise-85CD/README.md{anonymous-repository}}

音频时空融合的自适应证据加权

Adaptive Evidence Weighting for Audio-Spatiotemporal Fusion

摘要

Support