Room Impulse Response as a Prompt for Acoustic Echo Cancellation

2025-05-26

AI Summary

Data-driven acoustic echo cancellation (AEC) models suffer from poor generalization to unseen real-room scenarios, primarily due to the unobservable echo path and the absence of transferable priors. To address this, this work introduces room impulse responses (RIRs) as explicit prompts into an end-to-end AEC framework—marking the first such integration. We propose four multimodal fusion mechanisms for jointly encoding RIRs and speech features, and adopt a joint evaluation strategy using both simulated and real-world RIRs. Our core contribution lies in leveraging RIR embeddings to guide the model in learning robust, generalizable echo-path priors. Experiments demonstrate substantial improvements: PESQ increases by over 1.2, and ERLE improves by an average of 4.8 dB across unseen simulated and real acoustic environments—significantly outperforming RIR-agnostic baseline models.

Technology Category

• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

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.

Problem

• Improving AEC generalization in unseen echo scenarios
• Integrating RIR as training prompt for AEC
• Enhancing performance in real unobservable echo paths

Innovation

• Uses RIR as training prompt
• Explores four RIR fusion methods
• Improves generalization in unseen conditions