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arXiv Machine Learning By Alessia Milo, Georg G\"otz, Steinar Gu{\dh}j\'onsson, Daniel Gert Nielsen, Jesper Pedersen, Finnur Pind

Training DeepFilterNet with Accurate Room Acoustic Simulations Improves Single-Channel Speech Enhancement

Read the original on arXiv Machine Learning →

The study examines how the realism of synthetic room impulse response (RIR) datasets influences the training of DeepFilterNet3 for single‑channel speech enhancement. By comparing a DNS4 image‑source‑method RIR set with a higher‑fidelity hybrid wave‑based and geometrical acoustics RIR set, the authors find that the more realistic dataset consistently improves objective speech enhancement metrics and significantly reduces ASR word error rates on unseen measured RIRs. The results suggest that overall realism in synthetic acoustic training data enhances DeepFilterNet3’s generalization to new environments.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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