Adaptive Evidence Weighting for Audio-Spatiotemporal Fusion

• URL: http://arxiv.org/abs/2602.03817v1
• Date: Tue, 03 Feb 2026 18:21:13 GMT
• Title: Adaptive Evidence Weighting for Audio-Spatiotemporal Fusion
• Authors: Oscar Ovanger, Levi Harris, Timothy H. Keitt,
• Abstract summary: In bioacoustic classification, species identity may be inferred both from the acoustic signal and from context as location and season.<n>We introduce FINCH, an adaptive log-linear evidence fusion framework that integrates a pre-trainedtext audio classifier with a structuredtemporal predictor.<n>FINCH consistently outperforms fixed-weight fusion and audio-only baselines, improving robustness and error trade-offs.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 \textbf{F}usion under \textbf{IN}dependent \textbf{C}onditional \textbf{H}ypotheses (\textbf{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 \emph{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: \texttt{\href{https://anonymous.4open.science/r/birdnoise-85CD/README.md}{anonymous-repository}}

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