强大的规范盲系统识别与适应性语音脱声的应用
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Updated: Sep 14, 2025

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A Method to Study Adaptation to Left-Right Reversed Audition
Published on: October 29, 2018
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Zhimin Qiu1, Hongsen He1, Jingdong Chen2
1School of Information and Control Engineering and Robot Technology Used for Special Environment Key Laboratory of Sichuan province, Southwest University of Science and Technology, Mianyang 621010, China.
The Journal of the Acoustical Society of America
|July 23, 2025
概括
本研究为规范化的多通道频域最小平均平方 (NMCFLMS) 算法引入了一个可变规范化参数,增强其在杂,非静止环境中用于声系统识别和语音退声的稳定性.
科学领域:
- 信号处理 信号处理
- 声学系统识别 声学系统识别
- 语音增强器 语音增强器
背景情况:
- 规范化的多通道频域最小平均平方 (NMCFLMS) 算法被广泛用于多通道声系统的盲目识别.
- 它的性能受到对语音变化和噪音敏感的恒定规范化参数的限制.
- 这种灵敏度阻碍了在现实世界声学条件下的强度.
研究的目的:
- 通过引入可变规范化参数来开发一个更强大的NMCFLMS算法.
- 为了提高算法的适应性,以非静止的语音信号和添加噪声.
- 为了提高多通道盲人识别和语音脱声任务的性能.
主要方法:
- 提出一个可变的规范化参数,包括信号与噪声比,输出信号功率和波器长度.
- 实施基于自适应波器平均平方误差的调整参数更新机制.
- 使用多通道输出反向定理,将增强的算法应用于语音脱声.
主要成果:
- 变量调节NMCFLMS算法证明了对附加噪声和信号非静止性的强化稳定性.
- 适应性更新机制改善了随时变化的声系统的跟踪.
- 模拟证实了拟议方法在盲人系统识别和语音失声方面的有效性.
结论:
- 拟议的变量规范化方法显著提高了NMCFLMS算法的性能.
- 这种方法为多通道声系统识别和语音脱声提供了更可靠的解决方案.
- 这些发现通过使用现实世界声学测量的模拟来验证.
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