Smith College

This work ports multimodal Direct Preference Optimization (mDPO) from vision-language models to the audio domain, building preference pairs that contrast intact audio with acoustically perturbed audio (reversal, random noise, time and frequency masking) and training Qwen2-Audio-7B-Instruct, raising DCASE 2025 audio QA accuracy from 56.0% to 70.0% and AH Existence accuracy from 55.6% to 83.0%.

This work ports multimodal Direct Preference Optimization (mDPO) from vision-language models to the audio domain, building preference pairs that contrast intact audio with acoustically perturbed audio (reversal, random noise, time and frequency masking) and training Qwen2-Audio-7B-Instruct, raising DCASE 2025 audio QA accuracy from 56.0% to 70.0% and AH Existence accuracy from 55.6% to 83.0%.