Generative Data Augmentation Challenge: Synthesis of Room Acoustics for Speaker Distance Estimation

2025/01/22 by Jackie Lin, Georg Götz, Lin, Jackie +21 · 4 citations
Computer Science · #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Music and Audio Processing #Sound (cs.SD) #Speech Recognition and Synthesis #Speech and Audio Processing #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2501.13250

openalex publication_date 2025/01/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper describes the synthesis of the room acoustics challenge as a part of the generative data augmentation workshop at ICASSP 2025. The challenge defines a unique generative task that is designed to improve the quantity and diversity of the room impulse responses dataset so that it can be used for spatially sensitive downstream tasks: speaker distance estimation. The challenge identifies the technical difficulty in measuring or simulating many rooms' acoustic characteristics precisely. As a solution, it proposes generative data augmentation as an alternative that can potentially be used to improve various downstream tasks. The challenge website, dataset, and evaluation code are available at https://sites.google.com/view/genda2025.

Cited by

• Treble10: A high-quality dataset for far-field speech recognition, dereverberation, and enhancement
• HiFi-HARP: A High-Fidelity 7th-Order Ambisonic Room Impulse Response Dataset
• Room-acoustic simulations as an alternative to measurements for audio-algorithm evaluation
• MB-RIRs: a Synthetic Room Impulse Response Dataset with Frequency-Dependent Absorption Coefficients

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• Deep, data-driven modeling of room acoustics: literature review and research perspectives