Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation

📰 ArXiv cs.AI

Improve speaker distance estimation using generative impulse response augmentation and learn how to apply this technique to real-world audio processing tasks

advanced Published 5 May 2026

Action Steps

Generate room impulse responses using FastRIR
Augment sparse datasets with generated RIRs
Fine-tune speaker distance estimation models with augmented data
Evaluate model performance using metrics such as mean absolute error
Apply generative impulse response augmentation to real-world audio processing tasks

Who Needs to Know This

Audio engineers and researchers working on speaker distance estimation and room acoustics can benefit from this technique to improve model performance and accuracy

Key Insight

💡 Generative impulse response augmentation can significantly improve speaker distance estimation model performance

Key Takeaways

Improve speaker distance estimation using generative impulse response augmentation and learn how to apply this technique to real-world audio processing tasks

Full Article

Title: Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation

Abstract:
arXiv:2605.00721v1 Announce Type: cross Abstract: The Room Acoustics and Speaker Distance Estimation (SDE) Challenge at ICASSP 2025 explores the effectiveness of augmented room impulse response (RIR) data for improving SDE model performance. This challenge at GenDARA involves generating RIRs to supplement sparse datasets and fine-tuning SDE models with the augmented data. We employ the open-source fast diffuse room impulse response generator (FastRIR) conditioned only on speaker and listener loc

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