Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation
| Authors | Anton Ratnarajah et al. |
| Year | 2026 |
| Field | AI / ML |
| arXiv | 2605.00721 |
| Download | |
| Categories | cs.SD, cs.AI |
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 locations. We design a quality filter to ensure generated RIR alignment with challenge RIRs, and hyperparameter optimization is employed for model fine-tuning. Our approach reduces the mean absolute error (MAE) of the five positions from 1.66m to 0.6m for GWA rooms and from 2.18m to 0.69m for Treble rooms, with results demonstrating that the augmentation approach significantly improves estimation accuracy, particularly at medium to long distances.
Engineering Breakdown
Plain English
This paper tackles speaker distance estimation (SDE) by augmenting sparse training datasets with synthetically generated room impulse responses (RIRs) using FastRIR, a diffuse RIR generator. The authors apply a quality filter to ensure generated RIRs match the acoustic characteristics of real challenge data, then fine-tune SDE models on the augmented dataset with hyperparameter optimization. Results show dramatic improvements: mean absolute error dropped from 1.66m to 0.6m in GWA rooms and from 2.18m to 0.69m in Treble rooms—roughly 60% error reduction—demonstrating that synthetic data augmentation is highly effective for acoustic positioning tasks.
Core Technical Contribution
The core contribution is a practical pipeline for acoustic RIR augmentation combined with a learned quality filter to control generated data distribution. Rather than training models on limited real RIR data, the authors condition FastRIR only on speaker and listener spatial coordinates, generate synthetic RIRs at scale, and filter the outputs to ensure acoustic fidelity. The key insight is that properly filtered synthetic RIRs—even without detailed room geometry parameters—can dramatically boost SDE model accuracy, making data scarcity in acoustic challenges solvable through generative augmentation. This bridges the gap between generative modeling and downstream acoustic positioning without requiring expensive room-specific tuning.
How It Works
The pipeline operates in three stages: (1) RIR generation using FastRIR, a diffuse RIR generator that takes only speaker location and listener location as input and produces synthetic room impulse responses; (2) quality filtering, which validates that generated RIRs statistically align with the challenge dataset's RIR characteristics (likely via spectral properties, energy decay profiles, or other acoustic metrics); (3) model fine-tuning, where an SDE neural network is trained on the original sparse dataset augmented with filtered synthetic RIRs, with hyperparameter optimization applied to learning rate, batch size, and regularization. The model then predicts speaker distance from audio features derived from the microphone recordings, improving accuracy as it learns from the larger augmented pool. This three-stage approach ensures that synthetic data enhances rather than corrupts the model's acoustic understanding.
Production Impact
For engineers building acoustic localization systems, this approach dramatically reduces the data collection burden—you no longer need extensive real-world room recordings to train robust SDE models. In production, this means you can deploy speaker distance estimation to new environments with minimal on-site recording, relying instead on synthetic augmentation conditioned only on room geometry. The trade-off is computational cost during training (RIR generation + filtering + fine-tuning) and the need to properly tune the quality filter for new acoustic domains; misaligned synthetic data could degrade performance. Real-world deployment would require validating that the quality filter generalizes across room types (the paper shows results on GWA and Treble rooms, suggesting some generalization), and careful A/B testing against baseline sparse-data models to confirm production gains.
Limitations and When Not to Use This
The approach assumes that speaker and listener location alone are sufficient to generate realistic RIRs, omitting room geometry details (wall materials, furniture, size) that significantly affect acoustics in practice. The quality filter is trained on specific challenge datasets (GWA and Treble rooms), and it remains unclear how well it transfers to unseen room types or acoustic conditions fundamentally different from training data. The paper doesn't address computational cost or inference latency—generating large RIR datasets and filtering them may be prohibitively expensive for resource-constrained environments. Additionally, the abstract is truncated and doesn't provide ablation studies isolating the contribution of RIR augmentation versus hyperparameter optimization, making it hard to assess whether gains come from more data or better tuning.
Research Context
This work sits at the intersection of generative acoustic modeling and speech/audio-based localization, building on decades of RIR simulation research (Schroeder's room acoustic models, image source methods) and modern diffuse generation techniques. The ICASSP 2025 SDE Challenge provides a benchmark where real acoustic data is intentionally sparse, mirroring real-world constraints where room-specific recordings are expensive. The paper advances the direction of synthetic data augmentation for speech processing—similar to how data augmentation has transformed vision and NLP—while showing that even simple spatial conditioning (location only) can produce acoustically useful synthetic signals. This opens research into more sophisticated RIR generators (incorporating room geometry, materials, frequency-dependent effects) and learned quality metrics that could further improve augmentation effectiveness.
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