用房间冲激响应引导模型，提升回声消除在未知环境中的泛化能力
Room Impulse Response as a Prompt for Acoustic Echo Cancellation
- 将房间冲激响应作为训练提示，增强模型对未知回声路径的适应性
- 在模拟与真实场景下均显著优于基线模型，实测性能提升明显
- 适合需要跨场景部署的语音通信系统开发者参考
基于数据驱动的声学回声消除（AEC）方法通常在合成或受限的真实数据集上训练，导致在未见过的回声场景中性能下降，尤其在回声路径不可直接观测的真实环境中。本文提出一种新方法，通过引入房间冲激响应（RIR）作为关键训练提示，旨在提升AEC模型在未知条件下的泛化能力。我们还探索了四种RIR提示融合策略。综合评估涵盖未知条件下的模拟RIR和真实环境中的录制RIR，结果表明该方法显著优于基线模型，验证了RIR引导策略在增强模型泛化能力方面的有效性。
原文摘要 · Abstract (English)
Data-driven acoustic echo cancellation (AEC) methods, predominantly trained on synthetic or constrained real-world datasets, encounter performance declines in unseen echo scenarios, especially in real environments where echo paths are not directly observable. Our proposed method counters this limitation by integrating room impulse response (RIR) as a pivotal training prompt, aiming to improve the generalization of AEC models in such unforeseen conditions. We also explore four RIR prompt fusion methods. Comprehensive evaluations, including both simulated RIR under unknown conditions and recorded RIR in real, demonstrate that the proposed approach significantly improves performance compared to baseline models. These results substantiate the effectiveness of our RIR-guided approach in strengthening the model's generalization capabilities.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。