Training DeepFilterNet with Accurate Room Acoustic Simulations Improves Single-Channel Speech Enhancement
We investigate how the realism of synthetic room impulse response (RIR) datasets affects the training of DeepFilterNet3 for single-channel speech enhancement. We compare a DNS4 image-source-method (ISM) RIR dataset with a higher-acoustic-fidelity dataset generated using hybrid wave-based and geometrical acoustics simulation. Rather than isolating individual simulation factors, we compare complete RIR generation pipelines while keeping the enhancement model unchanged. Models are evaluated on unseen measured RIRs using objective speech enhancement metrics and downstream automatic speech recognition (ASR). Training with the higher-fidelity dataset consistently yields modest improvements in o...
| Authors | Alessia Milo, Georg Götz, Steinar Guðjónsson et al. |
|---|---|
| Primary category | eess.AS |
| Published | 21 August 2026 |
| Last updated at source | 21 August 2026 |
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