Adaptive Speech Enhancement and Signal Separation Using Robust Neural Network Estimation Techniques

使用鲁棒神经网络估计技术的自适应语音增强和信号分离

• 批准号: 9712346
• 负责人: Eric Wan
• 金额: $ 34.85万
• 依托单位: Oregon Health & Science University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-09-01 至 2001-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9712346&HistoricalAwards=false
• 关键词: Adaptive; Speech; Enhancement; Signal; Separation

The objective of this project is the development of robust methods for enhancing speech in real-world noisy environments. A neural network approach is developed for time-domain enhancement. The availability of only the noisy signal is assumed, in contrast to previous neural network approaches. Effectively, a sequence of neural networks is trained on the specific noisy speech signal, resulting in a nonstationary model that can be used to remove noise from the given signal. Two related frameworks are developed: (1) a neural state-space approach based on nonlinear extensions to Kalman filter theory and (2) a direct neural mapping from noisy speech to enhanced speech. This research represents fundamental enabling technology for interactive systems requiring robust speech communications. More traditional methods are often effective under ideal situations, but tend to degrade for real world nonstationary noise sources.

该项目的目标是在现实世界的嘈杂环境中增强语音的鲁棒方法的发展。 提出了一种时域增强的神经网络方法。 与以前的神经网络方法相比，假设只有噪声信号的可用性。 实际上，神经网络序列是在特定的噪声语音信号上训练的，从而产生可用于从给定信号中去除噪声的非平稳模型。 开发了两个相关的框架：（1）基于卡尔曼滤波理论的非线性扩展的神经状态空间方法和（2）从含噪语音到增强语音的直接神经映射。 这项研究代表 基本的使能技术， 需要鲁棒语音通信的交互式系统。 更传统的方法在理想情况下通常是有效的，但对于真实的世界非平稳噪声源往往会退化。