60年频域单声语音增强:从传统到深度学习方法
Chengshi Zheng1,2, Huiyong Zhang1,2, Wenzhe Liu1,2
1Key Laboratory of Noise and Vibration Research, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China.
Trends in hearing
|November 13, 2023
概括
本调查回顾了频域单声语音增强的传统和深度学习方法. 功能压缩对听力正常的听众有好处,但对听力受损的听众没有好处.
科学领域:
- 信号处理 信号处理
- 声学 声学 声学 声学
- 机器学习 机器学习
背景情况:
- 在频率领域的单元耳语音增强有着悠久的历史,在过去十年中由于深度学习而取得了重大进展.
- 传统方法和深度学习方法都被应用,后者表现更好.
研究的目的:
- 在频率领域提供传统和基于深度学习的单元语音语音增强技术的全面概述.
- 分析各种方法的基本假设,局限性和优势.
- 评估这些方法对正常听力和听力受损的听众的好处.
主要方法:
- 对传统和深度学习方法的现有文献的调查,用于频域单元语音语音增强.
- 使用WSJ+深度噪音抑制 (DNS) 挑战和语音银行+需求数据集对选定的方法进行比较评估.
- 对正常听力和听力受损的听众相关的客观指标的评估.
主要成果:
- 与传统方法相比,深度学习方法显著提高了性能.
- 客观测试表明,输入特征压缩对模拟正常听力听众有益,但对模拟听力受损听众不利.
- 提供了典型方法的统一比较.
结论:
- 单元神经语音增强技术,特别是那些采用深度学习的技术,提供了实质性的改进.
- 听众特定的适应,如功能压缩,对于优化性能至关重要.
- 确定了对单声语音增强的未来研究方向.
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