Robustness / performance improvements for deep learning-based speech enhancement against artifacts and distortion.
A multi-stage deep learning framework with a separator and improver addresses speech enhancement artifacts and distortions, enhancing speech quality by using autoencoders and neural networks to refine denoised signals.
Patent Information
- Application Number
- JP2024165835
- Authority / Receiving Office
- JP · JP
- Patent Type
- Patents
- Current Assignee / Owner
- Priority Date
- 2021-06-08
- Filing Date
- 2024-09-25
- Publication Date
- 2026-05-21
- Estimated Expiration
- 2042-03-17
AI Technical Summary
Existing deep learning-based speech enhancement techniques suffer from artifacts and distortions, particularly when dealing with different acoustic environments, leading to suboptimal performance.
A multi-stage deep learning-based framework comprising a separator and an improver, where the separator performs an initial denoising step, and the improver reduces distortions and artifacts introduced by the separator, utilizing autoencoders, recurrent neural networks, and generative models to refine the output.
The framework effectively reduces artifacts and distortions, improving the perceptual quality of speech signals by enhancing the speech component and suppressing noise, even in challenging acoustic conditions.
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Abstract
Citation Information
Patent Citations
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