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神经-WDRC:一种深度学习的广动态范围压缩方法,与可控降噪相结合,用于助听器
Huiyong Zhang1,2, Brian C J Moore3, Feng Jiang1
1Key Laboratory of Noise and Vibration Research, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China.
Trends in hearing
|January 27, 2025
概括
一种新的深度学习方法Neural-WDRC通过整合宽动态范围压缩 (WDRC) 和降噪来提高助听器的性能. 这种方法提高了语音清晰度和听力舒适度,特别是在杂的环境中.
科学领域:
- 听力学 听力学是指听力学.
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 助听器使用宽动态范围压缩 (WDRC) 进行放大和降噪,以提高语音可理解性和舒适性.
- 在当前助听器中连续实施WDRC和降噪可以扭曲语音和噪声振幅调制模式.
研究的目的:
- 引入Neural-WDRC,这是一种用于整合降噪的新型深度学习方法,以及助听器中的WDRC.
- 为了评估神经-WDRC与传统压缩技术相比的有效性.
主要方法:
- 一个两阶段的,低复杂度的深度学习网络 (神经-WDRC) 被开发出来,以分别估计和处理语音和噪音组件.
- 快速作用的压缩应用于估计的语音,缓慢作用的压缩应用于估计的噪音,允许可控制的剩余噪音.
- 基于的处理确定了基于当前和前一个的输出.
主要成果:
- 客观测量表明Neural-WDRC在非静止噪声条件下有效地减轻了负面的语音噪声相互作用.
- 听力测试表明,人们更喜欢神经WDRC,而不是传统和SNR意识的压缩方法,用于非静止噪声中的语音.
结论:
- 神经WDRC为助听器的同时WDRC和降噪提供了一个有希望的方法.
- 该方法可以提高听力和听力体验,特别是在具有挑战性的,动态的声学环境中.
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