Knowledge-Augmented Vision Language Models for Underwater Bioacoustic Spectrogram Analysis

Ragib Amin Nihal; Benjamin Yen; Takeshi Ashizawa; Kazuhiro Nakadai

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arXiv · 2509.05703

Knowledge-Augmented Vision Language Models for Underwater Bioacoustic Spectrogram Analysis

Ragib Amin Nihal·Benjamin Yen·Takeshi Ashizawa·Kazuhiro Nakadai

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Abstract

Marine mammal vocalization analysis depends on interpreting bioacoustic spectrograms. Vision Language Models (VLMs) are not trained on these domain-specific visualizations. We investigate whether VLMs can extract meaningful patterns from spectrograms visually. Our framework integrates VLM interpretation with LLM-based validation to build domain knowledge. This enables adaptation to acoustic data without manual annotation or model retraining.

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Ragib Amin Nihal, Benjamin Yen, Takeshi Ashizawa, Kazuhiro Nakadai. 2025-09-06. Knowledge-Augmented Vision Language Models for Underwater Bioacoustic Spectrogram Analysis. https://arxiv.org/abs/2509.05703

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