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DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement

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arxiv 2305.08227 v1 pith:IXLCZNZR submitted 2023-05-14 eess.AS cs.CLcs.SD

Hendrik Schröter , Tobias Rosenkranz , Alberto N. Escalante-B. , Andreas Maier This is my paper · ORCID
classification eess.AScs.CLcs.SD
keywords speechenhancementdeepfilternetableadvantagecorrelationsdomainreal-time
verification ladder T0 review T1 audit T2 compute T3 formal
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Multi-frame algorithms for single-channel speech enhancement are able to take advantage from short-time correlations within the speech signal. Deep Filtering (DF) was proposed to directly estimate a complex filter in frequency domain to take advantage of these correlations. In this work, we present a real-time speech enhancement demo using DeepFilterNet. DeepFilterNet's efficiency is enabled by exploiting domain knowledge of speech production and psychoacoustic perception. Our model is able to match state-of-the-art speech enhancement benchmarks while achieving a real-time-factor of 0.19 on a single threaded notebook CPU. The framework as well as pretrained weights have been published under an open source license.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DPDFNet: Boosting DeepFilterNet2 via Dual-Path RNN

    cs.SD 2025-12 conditional novelty 4.0 of 10

    DPDFNet inserts dual-path RNN blocks into DeepFilterNet2's encoder, adds an over-attenuation loss and long-context fine-tuning, and reports superior causal speech enhancement on a 12-language low-SNR test set.

  2. Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns

    eess.AS 2025-09 conditional novelty 4.0 of 10

    A 0.45M-parameter causal UNet-style denoiser with look-back frames and GRUs reports 22.34 dB SI-SDR on full-band VCTK data and RTF 0.014 on a Pixel 7.

  3. A Framework for Robust Speaker Verification in Highly Noisy Environments Leveraging Both Noisy and Enhanced Audio

    eess.AS 2025-08 conditional novelty 4.0 of 10

    A Siamese MLP that fuses speaker embeddings from noisy and DeepFilterNet-enhanced speech cuts speaker verification error at SNR -10 dB and below, while degrading performance near 0 dB.

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