Transient Noise Removal via Diffusion-based Speech Inpainting
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918123834179584 |
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| author | Moradi, Mordehay Gannot, Sharon |
| author_facet | Moradi, Mordehay Gannot, Sharon |
| contents | In this paper, we present PGDI, a diffusion-based speech inpainting framework for restoring missing or severely corrupted speech segments. Unlike previous methods that struggle with speaker variability or long gap lengths, PGDI can accurately reconstruct gaps of up to one second in length while preserving speaker identity, prosody, and environmental factors such as reverberation. Central to this approach is classifier guidance, specifically phoneme-level guidance, which substantially improves reconstruction fidelity. PGDI operates in a speaker-independent manner and maintains robustness even when long segments are completely masked by strong transient noise, making it well-suited for real-world applications, such as fireworks, door slams, hammer strikes, and construction noise. Through extensive experiments across diverse speakers and gap lengths, we demonstrate PGDI's superior inpainting performance and its ability to handle challenging acoustic conditions. We consider both scenarios, with and without access to the transcript during inference, showing that while the availability of text further enhances performance, the model remains effective even in its absence. For audio samples, visit: https://mordehaym.github.io/PGDI/ |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_08890 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Transient Noise Removal via Diffusion-based Speech Inpainting Moradi, Mordehay Gannot, Sharon Audio and Speech Processing Sound In this paper, we present PGDI, a diffusion-based speech inpainting framework for restoring missing or severely corrupted speech segments. Unlike previous methods that struggle with speaker variability or long gap lengths, PGDI can accurately reconstruct gaps of up to one second in length while preserving speaker identity, prosody, and environmental factors such as reverberation. Central to this approach is classifier guidance, specifically phoneme-level guidance, which substantially improves reconstruction fidelity. PGDI operates in a speaker-independent manner and maintains robustness even when long segments are completely masked by strong transient noise, making it well-suited for real-world applications, such as fireworks, door slams, hammer strikes, and construction noise. Through extensive experiments across diverse speakers and gap lengths, we demonstrate PGDI's superior inpainting performance and its ability to handle challenging acoustic conditions. We consider both scenarios, with and without access to the transcript during inference, showing that while the availability of text further enhances performance, the model remains effective even in its absence. For audio samples, visit: https://mordehaym.github.io/PGDI/ |
| title | Transient Noise Removal via Diffusion-based Speech Inpainting |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2508.08890 |