Focus Then Listen: Exploring Plug-and-Play Audio Enhancer for Noise-Robust Large Audio Language Models

TL;DR

This paper introduces Focus-Then-Listen (FTL), a plug-and-play audio enhancer that significantly improves noise robustness of large audio language models without requiring retraining or task-specific noisy data.

Contribution

The paper presents a novel, modular audio enhancement method that enhances LALMs' robustness to noise through a speech/non-speech separation and modality-aware fusion, without retraining the models.

Findings

FTL improves LALMs' performance across various noise conditions.

FTL does not require fine-tuning of the underlying models.

FTL is effective across multiple LALMs and tasks.

Abstract

Large audio language models (LALMs) are a class of foundation models for audio understanding. Existing LALMs tend to degrade significantly in real-world noisy acoustic conditions where speech and non-speech sounds interfere. While noise-aware fine-tuning can improve robustness, it requires task-specific noisy data and expensive retraining, limiting scalability. To address this issue, we propose Focus-Then-Listen (FTL), a plug-and-play audio enhancer that improves LALMs' noise robustness. Specifically, FTL first separates the input waveform into speech and non-speech, and a modality router is applied to predict the target audio modality (e.g., speech) based on the user's instruction. Finally, a modality-aware fusion block generates a task-adaptive enhanced signal for improved downstream perception and reasoning. Experiments across multiple LALMs and tasks show that FTL improves performance across different noise levels without fine-tuning on LALMs.