Focus Then Listen: Exploring Plug-and-Play Audio Enhancer for Noise-Robust Large Audio Language Models
Submitted to Interspeech 2026
Submitted to Interspeech 2026
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.
Demos
For demonstrations, please click the "demos" button at the upper right corner.
Detailed code and data will be available soon.
Prompts
Prompt for the LLM-based modality router in FTL:
You are an expert in audio understanding and multimodal reasoning. Your task is to decide what audio input should be provided to a Large Audio Language Model (LALM) in order to best accomplish a user’s instruction. The audio has been separated into three available inputs: speech: contains spoken voice content only; non-speech: contains non-speech acoustic events only; mixture: contains the original unfiltered audio. You should select the input that maximizes task-relevant information, based on the user’s instruction. Guidelines: 1. You should ONLY choose 'speech' when speech information alone is clearly sufficient to solve the task, AND non-speech provides no meaningful additional information. 2. You should ONLY choose 'non-speech' when non-speech audio alone is clearly sufficient to solve the task, AND speech provides no meaningful additional information. 3. In ALL other cases — including uncertainty, partial usefulness of both modalities, or when you cannot strictly rule out one modality — you MUST choose 'mixture'. Additional Domain Rules: - Speech is required for linguistic content, speaker intent, emotion, or dialogue understanding. - Non-speech includes environmental sounds and vocal non-linguistic sounds (e.g., laughter, sneeze, cough). Respond with only one word: speech, non-speech, or mixture. Do not provide explanations. User Instruction: [the user's instruction].
The User's Instruction for the Automatic Speech Recognition (ASR) Task:
Transcribe the speech into text, without any further explanation.
The User's Instruction for the Audio Tagging (AT) Task:
You are an expert in sound events classification. I will give you an audio recording. Please carefully analyze the sound events in this audio. Ignore speech and focus only on non-speech sound events. Output only one line, no explanations. List events detected in the audio, separated by a semicolon and a space. If no event is detected, output: None.