帕雷托优化的非负矩阵因子化方法来清理喉语音信号
Rytis Maskeliūnas1, Robertas Damaševičius1, Audrius Kulikajevas1
1Faculty of Informatics, Kaunas University of Technology, 44249 Kaunas, Lithuania.
Cancers
|July 29, 2023
概括
这项研究引入了一种新的方法,使用帕雷托优化的深度学习和非负矩阵分解 (NMF) 来清理受损的语音信号. 这种方法有效地减少噪音,同时保持语音质量,用于语音识别等应用.
科学领域:
- 信号处理 信号处理
- 机器学习 机器学习
- 语音技术 语言技术
背景情况:
- 清洁语音障碍对于包括语音识别,电信和辅助技术在内的应用程序至关重要.
- 现有的方法可能难以平衡降噪与语音质量维护.
研究的目的:
- 提出一种新的方法,结合帕雷托优化的深度学习和非负矩阵因子化 (NMF) 以有效地减少语音障碍的噪音.
- 为了在噪音清理过程中保持所需语音信号的质量.
- 在降噪,语音质量和计算效率之间实现平衡.
主要方法:
- 计算光谱图并从噪音语音中提取频率统计数据.
- 根据所需的噪声灵敏度计算和平滑噪声信号面罩.
- 应用帕雷托优化的NMF来分解和重建语音谱图以减少噪音.
主要成果:
- 提出的方法有效地减少了受损语音信号中的噪音.
- 在降噪过程中,语音质量得到保护.
- 实验结果证明了该方法的有效性,特别是在喉语音信号方面.
结论:
- 新的帕雷托优化深度学习和NMF方法为清理语音障碍提供了一个有希望的解决方案.
- 该方法成功地平衡了噪音抑制,语音质量和计算效率.
- 这种技术在语音处理和辅助技术中对现实世界的应用具有重大潜力.
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